# QAEverest — Full Site Content for LLMs > AI-Powered Test Case Generation & Quality Assurance Automation Platform This file is the long-form companion to https://qaeverest.ai/llms.txt. It is generated at build time from the live marketing pages, so it always matches what is currently published. Each section below is one public page. Canonical site: https://qaeverest.ai/ Pages included: 49 --- # AI Test Case Generation & QA Automation Platform | QAEverest URL: https://qaeverest.ai/ Summary: Turn user stories into test cases in seconds, then automate API, web UI, mobile and performance testing — one AI platform for QA. Skip to main content ✦ AI Powered & Secured Turn User Stories into Running Test Suites From Jira, GitHub, Figma or plain English Your browser does not support the video tag. Try it live — no signup ## Paste a user story. Watch the engine work. This is the same live AI engine our customers run — not a canned demo. Refine a story or generate test cases, free, right here. Refine a user story Generate test cases Try a sample: E-commerce checkout Banking login SaaS onboarding 0/4000 Refine my story — free No signup · No credit card AI test automation, end to end # Your tests break every sprint. Ours repair themselves. QAEverest turns a user story into a running suite — then keeps that suite alive through redesigns, and tells you exactly why a run failed instead of handing you a screenshot and a stack trace. Start free — no credit card Book a demo Free tier available · No credit card required 10X Acceleration in Test Case Creation and Automation 88%+ Accuracy in Test Case Generation and Automation 50X Faster User Story to Automation Pipeline 90% Better Coverage of Complex and Edge Case Scenarios 80% Cost Savings in Test Design & Maintenance 99.9% Reliability in Test Case Quality & Execution Benchmarks measured across API, UI, Performance, Security, Accessibility, and Exploratory testing workflows. ## What QAEverest Can Do Constant innovation keeps your QA ahead of the curve. Every capability below is available today — end-to-end, on one platform. ### Time-Travel Replay Scrub frame-by-frame through any UI test run. AI root-cause analysis surfaces the exact failed step, screenshot, and network call. ### Test Health & ROI Dashboard Quantify automation value: healed locators, quarantined flaky tests, failure clusters, and engineer-hours saved — metrics your team and stakeholders both need. ### Autonomous Exploratory Testing Point at a URL and walk away. AI logs in, crawls the app, generates Gherkin flows, executes them live, and triages failures into Jira bug cards in one click. ### Visual Regression Detection Pixel-level screenshot comparison with baseline management. Catch layout regressions across builds before users ever see them. ### Multi-Platform TCM Export Push test cases to TestRail, Zephyr Scale, Qase, QTest, PractiTest, or Azure Test Plans in one click — no copy-paste, no re-entry, no sync drift. ### Quality Command Center One ship-confidence score — functional pass-rates, RTM coverage, flakiness, and CI release gates — so release decisions are data-driven, not gut-driven. ### AI Copilot Assistant Ask "Where are my coverage gaps?" or "Run my last failed suite" in plain English. Copilot streams live tool actions and replies with structured answers. ### Figma Design Import Import live Figma frames directly into test generation. Design specs become test coverage automatically — no manual transcription, no drift. ### Import Your Automation Suite Connect a GitHub, GitLab or Bitbucket repo and turn your existing Playwright, Cypress, Selenium or pytest tests into reviewable, self-healing test cases — folders mirror your repo, page objects are flattened, nothing runs until you approve it. What sets QAEverest apart ## Capabilities built to give your team the edge Test generation, cross-browser grids and visual diffing are table stakes now — every platform has them, including this one. These four are where the work actually went. Self-maintaining suites ### The suite survives the redesign // after a component rewrite ✗ #pay-now not found ◆ re-anchored #checkout-pay ◆ assertion unchanged ✓ re-run passed When the application changes underneath a test, the run re-anchors the broken step and keeps the original assertion rather than handing you a red build to triage by hand. Repairs are logged and reversible See UI automation Time-travel debugging ### Scrub back to the moment it broke 00:00 failure at 00:07.4 Console, network and DOM state are captured across the whole run, so a failure opens on the exact step that caused it instead of a screenshot of the end state and a guess. No re-run needed to reproduce Read the deep dive Requirements traceability ### See the coverage you don't have 36 requirements 5 gaps Every requirement mapped to the tests that verify it and — more usefully — the ones nothing covers at all, ranked by business risk rather than reported as a percentage. Gap analysis, not a coverage number See traceability In your editor ### Testing without leaving the IDE $ qaeverest "cover the refund flow" → 14 cases · 3 edge · 2 negative → suite created, run queued // JetBrains · VS Code · Cursor An MCP server and a published JetBrains plugin put generation and execution inside the editor your developers already have open, so tests get written where the code is. qaeverest-mcp · JetBrains Marketplace See the public API The problem we set out to solve ### QA doesn’t break in one place. It breaks in four . Most tools fix a quarter of the problem — and the other three quietly absorb whatever time the first one saved. QAEverest was built to close all four. 01 Authoring “Writing test cases from a story takes days — and the story has changed by the time we finish.” QAEverest Tests generated from user stories and real production journeys — across UI, API, mobile, security and performance, in one place. 02 Maintenance “A button moved and forty tests went red. None of them found a bug.” QAEverest Suites that re-crawl the app, detect drift and repair their own locators — flaky tests quarantined, failures clustered by root cause. 03 Diagnosis “A test failed overnight. All we have is a screenshot and a stack trace — go reproduce it yourself.” QAEverest Console and network captured for every step, replayable frame by frame, with AI reading the run and explaining in plain language why it broke. 04 The decision “The release call comes down to a green tick and somebody's gut feeling.” QAEverest Functional results, coverage and stability fused into one ship-confidence score that can gate a release — every requirement traced, the gaps surfaced. And all of it where the work already happens — VS Code, JetBrains, Jira, GitHub or a public API. A tool that solves a real problem in a place nobody goes hasn’t solved the problem. Fits what you already run ## Your suite leaves in a format you own Export to code you can read, run and keep. If you leave us, the tests keep working — that’s the point. ### Export Engines Playwright Selenium Cypress ### Languages Java JavaScript TypeScript Python C# ### Source & Planning Jira GitHub GitLab Azure DevOps Figma ### CI GitHub Actions Jenkins GitLab CI Azure Pipelines ### Device Farms BrowserStack Sauce Labs LambdaTest Self-hosted grid ### Editors JetBrains VS Code Cursor Windsurf MCP ### Browsers Chrome Firefox Edge Safari Chromium ## Our Approach Built for high-stakes industries ## For Industries Where Every Release Carries Real Risk From FinTech to the factory floor — QAEverest.ai hardens the flows your business runs on. Financial Services Payments · KYC · Fraud Retail & E-commerce Checkout · Inventory Healthcare HIPAA · EHR · A11y Insurance Quote · Bind · Claims Utilities & Telecom Billing · Provisioning ERP & Manufacturing Order-to-cash · Supply Payments · KYC · Fraud ### Financial Services Ship faster without breaking trust. Validate payment rails, KYC and fraud checks end to end — with audit-ready evidence on every release. Payment rails KYC / AML Fraud rules Audit trail Checkout · Inventory ### Retail & E-commerce Peak-season ready. Test omni-channel checkout, inventory sync and payment flows under realistic load before Black Friday finds the bugs. Omni-channel checkout Inventory sync Load & scale Promotions HIPAA · EHR · A11y ### Healthcare Compliance you can prove. Cover HIPAA-sensitive journeys, EHR integrations and accessibility from day one — not the audit before launch. HIPAA flows EHR / HL7 WCAG a11y Consent Quote · Bind · Claims ### Insurance Quote-to-bind, verified. Exercise complex rating rules, claims workflows and document generation across every edge case that matters. Rating engine Claims flow Doc generation Underwriting Billing · Provisioning ### Utilities & Telecom Reliability at scale. Validate billing, provisioning and outage journeys across millions of accounts — and keep them green every sprint. Billing Provisioning Outage flows Self-serve Order-to-cash · Supply ### ERP & Manufacturing Keep operations moving. Continuously regression-test order-to-cash, inventory and supply-chain integrations so a release never stops the line. Order-to-cash Inventory Supply chain Integrations 🔒 Security & Trust ## Enterprise-grade security, built in From SSO to audit trails, QAEverest.ai gives security teams the controls they expect. ### Single Sign-On (SAML 2.0) Bring your own identity provider — Okta, Azure AD or Google Workspace — with just-in-time user provisioning. ### Role-Based Access Control Granular Viewer, Member, Lead and Admin roles, with project-level overrides for least-privilege access. ### Audit-Ready Activity Logs Every change and sign-in is captured with the user, IP address and timestamp for full accountability. ### Multi-Tenant Isolation Each organisation's data is logically isolated and access-scoped — your tests never mix with anyone else's. ### Encrypted Credentials Passwords are hashed with bcrypt and sessions are protected with signed, server-stored tokens. ### Configurable Security Policy Admins set session timeout, IP allow-lists, password rules and account-lockout thresholds per organisation. Per-organisation access control · Encrypted credentials · Full audit trail ## Whatever your automation goals may be, QAEverest.ai empowers you to achieve them with ease. ## Ready to Ace Software Testing with QAEverest.ai ? Start Free — No Credit Card Required ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QA Platform Overview: Test Generation to Release | QAEverest URL: https://qaeverest.ai/productoverview Summary: One platform for the full QA lifecycle: AI test-case generation, importing your existing automation repository, API, UI and mobile automation, visual regression, self-maintaining suites, business risk analysis, traceability and a public API. Skip to main content 21 AI-powered capabilities # One platform for the entire QA lifecycle AI-assisted from requirement to release — plan, generate, automate, assess and ship, with enterprise-grade trust built in. Here’s everything QAEverest can do. Start free Explore features 98% cost savings 10× faster test creation 90% test coverage 88%+ AI accuracy 6 TCM export targets SOC 2 enterprise-ready HOW IT FLOWS 1 Refine clear, testable story 2 Generate test cases, any source 3 Automate API, UI & mobile suites 4 Assess quality & business risk 5 Ship confidence score 6 Integrate API, CI/CD & plugins THE FULL MAP ## Everything QAEverest does Rows marked AI are powered by AI. A Plan & Generate - Story Refinement AI Rough idea → clear, testable story with acceptance criteria. - Generate Test Cases AI From story, Jira, ClickUp, Postman & Swagger — TDD or BDD. - RAG Personalization AI Learns from your past suites & repo context for sharper, on-brand tests. - GitHub Branch / PR Import AI Summarises a branch or PR diff into a ready-to-test story. - Figma Design Import AI Turn a Figma frame into a story & test coverage. - By Repository AI NEW Import an existing Playwright, Cypress, Selenium or pytest repo — scripts become editable test cases you review first. - History & TCM Export Saved by source; export to 6 TCM platforms + XLSX/BDD. B Automate & Run - API Automation REST, SOAP & GraphQL; schema validation & data injection. - UI Automation AI AI agents drive the browser with self-healing locators. - Point to Element AI Fix a flaky step by pointing at the element — locator re-pinned. - Mobile Testing AI Native, hybrid & web apps on real devices & emulators. - Cloud Device Farm Run on real devices & browsers via BrowserStack, Sauce Labs & LambdaTest. - Test Sets Pick test cases by tag across suites into a fast pre-regression gate. - Hybrid UI + API Steps NEW Flip any step between browser and HTTP — one suite covers both layers. - Suite Prerequisite Steps NEW Author login or setup once per suite; every test inherits it, any test can opt out. - Suite Folders & Modules Nested folders per project across UI, API & Mobile — mirrored from a repo import. - Scheduler Run suites on a schedule, or trigger them from your CI/CD pipeline. - Test Data & Entity Library Environments, global variables & reusable entity records injected at run time. C Insight & Debug - Visual Regression AI Pixel + SSIM screenshot diff vs baseline; auto-flags drift. - Business Risk Analysis AI Scores each failure Critical→Low with an impact summary. - Time-Travel Replay AI Scrub a failed run; AI pinpoints the failed step & cause. - Test Health & ROI AI Flaky detection, failure clusters & hours saved. - Self-Maintaining Suites AI Crawls your app, spots what changed & repairs stale steps — Suite Health scores the rest. - Report a Bug File a failed test straight to Jira or ClickUp with its screenshots & video attached. - Decision-Making Reports AI Shareable PDF reports — results, visual diffs, risk & coverage for go/no-go calls. D Assess Quality - Performance Load, stress, spike & soak tests. - Security Headers, SSL/TLS & vulnerability scans. - Accessibility WCAG 2.2 audits to AAA, contrast & focus heatmaps, scheduled monitoring. - Traceability AI Coverage matrix, RAG link suggestions & smart test selection from a PR. - Quality Center One Ship-Confidence Score: ship / caution / blocked. - Exploratory AI Autonomous agent: login, crawl, run & file bugs. E AI Assistants & Bots - AI Assistance AI Ask in plain language — answers, generates & builds suites. Attach docs or screenshots, download results as XLSX, PDF or DOCX. - PR-native QA Bot AI Auto-comments risk analysis on every GitHub pull request. - Jira Forge Plugin AI Generate AI test cases without leaving a Jira issue. F Developer Platform BUILD ON US - Public API (v1) AI Third-party integration: generate & automate over REST. - MCP Server qaeverest-mcp connects QAEverest to AI agents & IDEs. - API Keys & Library Create keys; browse documented, ready-to-call endpoints. - Team API Users Many developers per account; rotate & revoke keys independently. - IDE Plugins Generate & execute from VS Code, JetBrains, Claude Code & Cursor. - Code Export Export a suite as runnable Playwright, Selenium or Cypress code. - CI/CD Integrations Trigger GitHub, GitLab, Jenkins, Azure & Bitbucket. G Enterprise Trust Layer - Single Sign-On (SAML) Sign in with your IdP; accounts auto-provisioned. - Roles & permissions Viewer · Member · Lead · Admin, per-project overrides. - Audit logs Exportable record of who did what, when & from where. - Security policy Account lockout & session-timeout controls. - On-premises deployment Run the whole stack inside your own network. H Account & Help - Plan & Payment Plans, subscriptions, payments & credit log. - Profile Personal details, offers & password. - Settings Jira & ClickUp integration credentials — set once. - Support Reach the QAEverest team for help. - Sign Out Securely end your session everywhere. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # /generatetestcase URL: https://qaeverest.ai/generatetestcase Summary: Generate AI-powered test cases for functional, system, API, security and performance testing — from user stories, documents, Figma, or by importing your existing automation repository. QAEverest builds comprehensive coverage with minimal effort. Skip to main content Generate Comprehensive Test Cases in Seconds with AI & our proprietary algorithm Import your suite — start free # Generate Comprehensive Test Cases in Seconds with AI & our proprietary algorithm Upload a user story, document, or Figma file — or connect the Git repository of an existing automation suite — and instantly get Functional, System, API, Security & Performance test cases, ready to export to Jira, TestRail, Azure and more. Every generation includes an automated Business Risk Assessment that scores and prioritizes what to test first. - Functional - System - API - Security - Performance Seamlessly integrates with the tools your team already uses 0 % Accuracy Achieved 0 % Time Saved 0 % Secured 0 + Testing Types Covered ## Discover the powerful capabilities of QAEverest.ai ## Effortless Data Extraction ### Upload Files, Images, Figma, docs, csv, xlsx Upload files, images, Figma designs, docs, or spreadsheets, and let QAEverest.ai's AI turn them into a structured, refinement-ready user story — with no manual data entry. ## Flexible Format Choice ### TestRail, QTest, QBase, Azure, PractiTest, XLSX, and CSV Export your test cases in various formats to seamlessly integrate with your existing testing infrastructure. QAEverest.ai supports popular formats like Zephyr, TestRail, QTest, QBase, Azure, PractiTest, XLSX, and CSV. ## Project Management Integration ### Jira & ClickUp No need to disrupt your existing workflow. QAEverest.ai integrates seamlessly with your preferred project management tools like Jira and ClickUp. Generate test cases directly from tasks and keep everything centralized for maximum efficiency. ## Test Case Selection ### Choose the Right Test Cases We understand that every team has its own preferences. Choose between creating test cases in traditional format (TDD) or leveraging the collaborative power of Gherkin (BDD) format. QAEverest.ai provides flexibility to suit your team's needs. ## Swagger / Postman Test Generation ### Generate from API specs & collections Import a Swagger/OpenAPI specification or a Postman collection and QAEverest.ai automatically generates API test cases covering endpoints, parameters, payloads, and expected responses. ## Figma URL Test Generation ### Generate from a Figma design link Paste a Figma design URL and QAEverest.ai analyzes the screens and components to generate UI test cases instantly — no manual specification writing required. ## How It Works From raw requirement to automated execution — five steps. 1 ### Upload or Connect Drop in a user story, document, image, Figma file, CSV or XLSX — or connect a Git repository to import the automation scripts you already have. Our AI extracts the relevant details automatically. 2 ### Questionnaire Answer a few quick questions about scope, priorities, and edge cases so the AI tailors test cases precisely to your requirements. 3 ### AI Generates Pick your testing type and TDD or BDD format. AI builds comprehensive test cases in seconds. 4 ### Export Send test cases straight to Jira, ClickUp, TestRail, Azure, QTest, PractiTest, XLSX or CSV — no copy-paste required. 5 ### Proceed to Automation Take your generated test cases into API Automation or Web UI Automation. Set up your environment, create a suite, and execute on demand or trigger runs from your CI/CD pipeline. ## Already Have Automation? Bring It With You. Generate Test Cases → By Repository reads the automation scripts you already maintain and turns them into editable, self-healing QAEverest test cases — no rewrite, no copy-paste, and you review everything before it runs. - Playwright - Cypress - Selenium - WebdriverIO - pytest - JUnit / TestNG - NUnit - RSpec - Cucumber - Robot 1 ### Connect, don't upload Point QAEverest at a GitHub, GitLab or Bitbucket repository (read-only), pick a branch and a folder. A free preflight shows the frameworks, folders and estimated test cases before you spend a credit. 2 ### Page objects flattened Each test is read together with the page objects and helpers it imports, so the extracted steps are plain English — "Click the Sign in button" — not class and method names. 3 ### Review before anything runs Every test case carries a confidence score, the source lines it came from and any helper call the AI could not follow. Edit, accept or reject — one by one or in bulk. 4 ### Folders mirror your repo Proceed to Automation files the accepted cases into modules that match your repository directories, one suite per file, straight into UI, API or Mobile Automation. By Repository · review acme/webshop tests/e2e checkout cart.spec.ts 6 / 6 payment.spec.ts 9 / 11 auth login.spec.ts 4 / 4 Guest can complete checkout with a saved coupon 94% - Navigate to "/cart" - Click the "Checkout" button - Enter "SAVE10" in the Coupon field and click "Apply" - Verify the order total reads "$42.30" 12│ test('guest checkout with coupon', async ({ page }) => { 13│ await cart.open(); await checkoutPage.applyCoupon('SAVE10'); Preflight and review are free — extraction is billed per test case, so you only pay for what you keep. Import your suite — start free ## Every Type of Testing, Covered Functional System API Security Performance ## Functional Testing QAEverest significantly enhances this process by automating the creation of functional test cases, ensuring comprehensive coverage with minimal effort. - One-Click Test Case Generation - Maximum Coverage - Download Multiple Test cases in Excel Format - Covers Positive, Negative, Usability, Localization, Internationalization and Recovery. ## See It In Action Paste a user story below and preview the kind of test cases QAEverest generates. Your user story Generate Sample Test Cases Your generated test cases will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # /pricing URL: https://qaeverest.ai/pricing Summary: Discover QAEverest Skip to main content # PLANS & SUBSCRIPTIONS USD INR ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # API Test Automation: TDD, BDD & AI | QAEverest URL: https://qaeverest.ai/webapiautomation Summary: Automate TDD, BDD and AI-generated API test cases. Boost testing efficiency by up to 85% with parallel execution, CI/CD integration and in-depth reporting. Skip to main content # API automation: speed and reliability, simplified Generate, run and validate TDD, BDD and AI-authored API tests from a single workspace — with parallel execution, CI/CD hooks and insightful reports. Checkout API · suite run ● passing 42 tests 39 passed 3 failed Pass rate 93% Works with your stack — 20+ integrations Swagger Postman cURL GitHub Actions Jenkins GitLab Bitbucket Jira Slack ## Automate API test cases in TDD or BDD — written by you or generated by AI Comprehensive support for every testing approach, generated and validated automatically. ### Comprehensive test support Automate Test-Driven and Behavior-Driven Development test cases based on user stories and specifications. ### AI-generated test cases Seamlessly integrate test cases generated by QAEverest.ai to maximize coverage and efficiency. ### Direct test generation Generate API tests directly from OpenAPI/Swagger specs, Postman collections and other formats. ### One-click validation Validate API response status, headers and body instantly with built-in JSON Schema support. ## AI that writes, validates and expands your tests Let QAEverest.ai do the heavy lifting — from generation to validation to coverage. ### Natural language to tests Turn user stories and specs into ready-to-run TDD & BDD API tests. ### Auto-generated assertions Status, headers and body checks — including negative and edge cases. ### Spec-driven import Generate suites from OpenAPI/Swagger specs and Postman collections. ### Coverage gap detection Surface untested endpoints and missing scenarios as your API grows. ## Environments, variables and authentication Configure once, run anywhere — securely, across every stage of your pipeline. ### Multiple environments Dev, QA, Staging and Production — each with its own base URL and configuration. ### Variables Environment-scoped and global {{variables}} resolved automatically at run time. ### Built-in authentication Bearer / OAuth2, Basic, API key, JWT and custom-header auth — set per environment. ### Reusable configuration Define auth and variables once and reuse them across every suite and run. ## Key features: powering your API testing ### Parallel execution & smoke tests - Run multiple tests concurrently to reduce overall execution time by up to 80%. - Spin up smoke suites from existing cases to validate critical paths fast. ### Test data management - Easily handle CSV, JSON and custom data injections. - Data-driven runs with parameterized example tables. ### Scheduling & CI/CD triggers - Recurring schedules — hourly, daily or weekly — plus one-time runs. - Webhook triggers for GitHub Actions, Jenkins, GitLab, Azure DevOps and Bitbucket. - Slack notifications on every run. ## Comprehensive assertions, automatically suggested Validate every response without boilerplate — QAEverest.ai proposes the right checks for each step. Status codes Response headers Body & JSON path JSON Schema Response time (SLA) Array & type checks JWT & auth Content search See it in action ## Rapidly test and validate API requests ## In-depth reporting: actionable insights at a glance ### Real-time dashboards Track pass rate, key metrics and trends instantly with visual, interactive panels. ### Detailed execution logs Access logs, failure analysis and error details to troubleshoot effectively and fast. ### Trends, history & health Run history with pass-rate trends, suite health and per-environment breakdown. ### Export & share Export reports to PDF and share results by email or Slack notification. Analyze and identify issues 40% faster with comprehensive analytics built into every report. ## Benefits: why choose QAEverest.ai for API automation? Boost testing efficiency by automating repetitive tasks. Expand test coverage across all API endpoints. Accelerate feedback loops for faster development cycles. Reduce costs with automation and resource savings. Enhance quality to deliver APIs with confidence. Achieve up to 30% faster time-to-market. ## Beautiful UI for effortless API automation ### Intuitive workflow Create, execute and analyze API tests with ease using a streamlined process that reduces complexity. ### Customizable dashboards Monitor tests in real time with widgets tailored to your metrics and preferences. ### Reduced learning curve Designed for speed, our UI cuts training time by half compared to traditional API testing tools. ## How it works 1 ### Set up environment Base URL, variables and auth. 2 ### Generate tests From stories, specs or AI. 3 ### Run in parallel Locally or in CI/CD. 4 ### Analyze & export Reports in HTML, PDF, CSV. ## See It In Action Paste a Swagger/OpenAPI or Postman collection URL and preview the chained test suite QAEverest generates and executes. Your Swagger / OpenAPI or Postman collection URL Generate Sample Suite Your generated API test suite will appear here. ## Results teams see with QAEverest 85% higher efficiency 80% less run time 40% faster triage 30% faster delivery ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Web UI Automation | QAEverest URL: https://qaeverest.ai/webuiautomation Summary: AI-powered UI automation: self-healing locators, visual assertions, 4-stage DOM resolution and Time-Travel root cause analysis on Chrome, Firefox, Safari and Edge. Skip to main content Web UI Automation # Self-Healing. Visually Verified. Fully Traced. QAEverest resolves element locators live from the DOM, repairs broken selectors automatically, validates assertions from screenshots, and traces every failure with AI-generated root cause analysis. Self-Healing Locators Visual Assertions Time-Travel RCA Start Free Trial Book a Demo checkout_flow.feature Chrome RUN #247 checkout_flow 6 steps ✓ Navigate to /cart stage3 · 0.94 ✓ Assert cart is not empty vision ✓ Click ‘Proceed to Checkout’ stage3 · 0.87 ↺ Click ‘Confirm Order’ SELF-HEALED ✓ Assert confirmation message ocr ✓ Verify email notification sent vision AI Summary 1 locator auto-healed. All 3 visual assertions confirmed by screenshot. ▶ Time-Travel RCA Ready 📹 2m 14s 60 % Faster Test Creation vs. traditional scripted automation 80 % Less Manual Scripting freeing QA capacity for exploratory work 50 % Reduction in Debugging via AI root cause analysis 4 + Browsers Supported Chrome, Firefox, Safari, Edge How It Works ## From Test Case to Verified Result Three phases, nine agents, zero hardcoded selectors. 1 Step 1 — Author ### Write Your Test Cases Author test cases in BDD (Gherkin) or plain-text TDD format using dynamic variables, Scenario Outlines, and data-driven entity datasets — all from the test case editor. - Scenario Outline with data-driven entity datasets - Dynamic placeholders resolved per environment (Dev / QA / Prod) - BDD steps mapped directly to browser actions by the AI pipeline Test case ready for execution 2 Step 2 — Execute ### AI Pipeline Runs 9 Agents Nine specialized agents handle intake, locator resolution, execution, self-healing, and visual assertion — zero hardcoded XPath or CSS selectors. - Locators resolved live from DOM with confidence scoring - Broken selectors auto-healed without manual intervention - Assertions validated from screenshots, not DOM selectors 9 agents · 0 hardcoded selectors 3 Step 3 — Analyze ### Analyze Every Result An AI-generated summary separates test failures from application failures, with a full execution trace, video recording, and root cause analysis per run. - Time-Travel RCA — screenshot, console, and network per step - Full .webm video recording linked from the report - Export PDF, HTML, or CSV; push results to CI/CD AI root cause · Video evidence · Export ready Platform Capabilities ## Everything the Platform Can Do From self-healing locators to frame-level root cause analysis — all built into a single execution pipeline. ### Self-Healing Locators When your app changes — new DOM structure, updated attributes, moved elements — the pipeline detects the broken step, re-inspects the live page, and resolves a working selector automatically without manual maintenance. No manual locator maintenance ### Visual Assertion Engine Assertions are validated against actual screenshots — not DOM selectors. Works on canvas-rendered UI, iframe-embedded content, and dynamic elements. Two-tier engine: OCR for text visibility, AI Vision for semantic checks. Works on any rendered UI Time-Travel RCA ### Root Cause Analysis at Every Frame Every execution generates a full trace: screenshot, console log, network activity, and page text captured at each step. AI narrates exactly when the failure occurred and what changed on screen. - Navigate to any failed step and view the exact screenshot at that moment - Console logs and network requests correlated to the failure frame - AI narrates what changed and why the assertion did not pass rca_trace — checkout_flow · step 7 5 ✓ Add to cart → button clicked 6 ✓ Navigate to /checkout 7 ✗ Assert "Order Total" visible SCREENSHOT AI Root Cause “Order Total” element not found in screenshot at step 7. Page shows loading spinner — checkout API response delayed > 4 s. DOM had element but was hidden via opacity:0. ### Data-Driven Execution One test case drives multiple entity datasets. Define different data for Dev, QA, and Production independently — one case, unlimited scenarios. ### App Crawl & Element Health Discovers all interactive elements across your app and detects structural changes between deployments — before your tests break. ### Multi-Environment Execution Execute the same test with environment-specific data across Dev, QA, and Production without maintaining separate test cases. ### Cross-Browser Execution Run the same test suite across Chrome, Firefox, Safari, and Edge from one trigger. ### Visual Regression SSIM diff compares screenshots against stored baselines with red-diff overlay. ### Integrated Reporting Export PDF, HTML, or CSV and push results directly to Jira. Under the Hood ## 4-Stage Locator Resolution No hardcoded XPath or CSS. Every locator is resolved live from the DOM with a measurable confidence score. Stage 0 ### ML Element Detection Object detection and text recognition scan the page screenshot to identify candidate interactive elements before the DOM is consulted. Object detection Text recognition Element candidates Stage 1 ### AI Vision Matching AI interprets the screenshot and matches the described element to the most visually accurate candidate identified in Stage 0. Visual matching Context reasoning Best-match selection Stage 2 ### DOM Resolver Maps fractional screen coordinates to the actual DOM node using elementFromPoint, extracting full HTML context for selector generation. elementFromPoint outerHTML DOM context Stage 3 ### Locator Output Generates ranked XPath and CSS selectors from DOM context, each with a confidence score. The highest-confidence selector drives execution. XPath CSS selector Confidence score Execution Report ## Understand Every Failure Before the Next Run The execution report gives step-level visibility into what happened, why it failed, and what to do about it — without re-running the test. ### Step-Level Execution Results Every step shows its status — Passed, Failed, Healed, or Skipped — alongside the duration, error message, screenshot captured at execution, and the locator stage that was used. Nothing is hidden behind a summary. ### Time-Travel Replay Navigate to any step in a failed run and view the screenshot, console errors, and network requests captured at that exact frame. AI narrates what changed on screen and why the assertion did not pass — no re-execution required. ### Failure Classification Each failure is classified as an Application bug, a Test issue (wrong locator or outdated step), or an Environment problem (timeout, network) — with a confidence score — so your team knows where to act. ### Fix Suggestions When a step fails due to a test-side issue — a renamed element, an outdated expected value — the platform proposes a corrected step with a confidence score. Suggestions are never auto-applied; you review and accept each one. execution_report — checkout_flow · step 7 of 9 STEP 7 FAILED Assert "Confirm Order" button is visible ElementNotFoundError: ‘Confirm Order’ not found after 4 retries · stage3 · xpath: //button[text()='Confirm Order'] · confidence 0.34 APPLICATION ISSUE confidence 0.91 AI Root Cause “Confirm Order” button was present in the DOM but hidden beneath a loading overlay — checkout API response exceeded the 3 s threshold. - POST /api/checkout returned 200 after 4.2 s (threshold 3 s) - Console: Uncaught TypeError in payment.js:142 — 'amount' undefined - Visible page text at failure: “Processing payment…” SUGGESTED FIX · TEST ISSUE Wait for network idle before asserting button visibility — step text updated to: “Wait for checkout to complete, then assert 'Confirm Order' is visible” confidence 0.78 · not auto-applied — review & accept console: 1 error network: 1 slow screenshot ✓ page text ✓ video ✓ ## See It In Action Write a test case in Gherkin and preview how the AI pipeline executes it in a real browser. Your Gherkin test case Preview Browser Run Your browser execution results will appear here. ## Start Testing What Your Users Actually See Self-healing execution, visual assertions, and AI root cause analysis — available from day one, no credit card required. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Mobile Test Automation | QAEverest URL: https://qaeverest.ai/webmobileautomation Summary: Mobile test automation for native, hybrid and web apps on Android & iOS — visual AI locators, self-healing tests, an exploratory agent and OCR-verified assertions. Skip to main content # Mobile testing, automated by AI Mobile automation is coming to QAEverest. Test native, hybrid and web apps on Android & iOS with an agentic engine that finds elements by vision, heals itself when the UI changes, and verifies results with OCR — across real devices, emulators and simulators. Explore Now Android iOS Tablets Run Tests 100% Passed AI Automated Self-healing What's coming ## AI does the hard parts of mobile testing The capabilities below are what will set mobile automation apart — built on a multi-agent, vision-first engine. Vision AI ### AI Visual Locators Advanced AI-powered visual recognition accurately identifies mobile app elements and user interactions, ensuring reliable test execution across different screens and devices. Resilient ### Self-Healing Tests When a screen changes between releases, locators repair themselves automatically — runs keep passing instead of breaking on every app update. Agentic ### Autonomous Exploratory Agent Point it at your app and it crawls screens on its own, discovering flows and generating runnable tests — coverage without writing a script. Verified ### AI Assertions + OCR Outcomes are verified visually — an assertion agent plus OCR confirm on-screen text and state, catching what selector-only checks miss. 4.4–14 Android versions 8–17 iOS versions Tablets iPad & Android tablets 3 modes Physical · Emulator · Simulator Cross-Platform ## Unified cross-platform support One platform for native, hybrid and web apps across every mobile OS your users run. ### Comprehensive Coverage - Test native, hybrid, and web apps on Android and iOS devices. - Run a single suite across phones and tablets. ### Efficiency Boost - Reduce test fragmentation with one unified mobile platform. - Increase test coverage through consolidated efforts. Get running fast ## Zero-configuration setup Skip the toolchain headaches — everything you need is installed and ready in minutes. ### Single-Click Install Install all necessary dependencies with just one click. ### Instant Deployment Eliminate complex setup and dependency conflicts, getting started in minutes. ### Time Savings Achieve a 95% reduction in setup time compared to traditional methods. Planned coverage ## Device & version coverage Search the planned grid of supported mobile platforms and filter by ecosystem. One test suite, the whole device matrix. All Android iOS / iPadOS Android Phones At launch 4.4 → 14 · Pixel, Samsung, OnePlus Native · Hybrid · Web Physical · Emulator Android Tablets At launch Galaxy Tab · Lenovo · Fire OS Native · Hybrid · Web Physical · Emulator iPhone (iOS) At launch 8 → 17 · iPhone 6 → 15 Pro Native · Hybrid · Web Physical · Simulator iPad (iPadOS) At launch iPad · iPad Air · iPad Pro Native · Hybrid · Web Physical · Simulator ### Broad Device Range Test across Android versions 4.4 to 14 and iOS versions 8 to 17. ### Identify Issues Faster Quickly pinpoint and address version-specific issues before they impact users. ### Ensure Compatibility Verify consistent performance across all target devices and OS versions. ### Boost App Ratings Increase app store ratings by 20% by catching version-related bugs. All in one place ## Integrated environment Physical devices, emulators and simulators — managed from a single, unified hub. ### Physical Devices Seamlessly manage and integrate your physical testing devices. ### Emulators Incorporate and control emulators for virtual testing scenarios. ### Simulators Utilize simulators to expand your testing coverage efficiently. ### Unified Hub Streamline workflows with a centralized platform, reducing overhead by 60%. Fits your workflow ## Runs on real-device clouds Run your mobile suites on real devices in the cloud — BrowserStack, Sauce Labs and LambdaTest — with your app uploaded automatically at run time. ### BrowserStack Run on BrowserStack's real-device cloud — connect your credentials and pick from its live device catalog. ### Sauce Labs Run on Sauce Labs' real-device cloud — your app uploads automatically at run time, no local Appium required. ### LambdaTest Run across LambdaTest's real-device cloud with a live catalog for platform, device and OS version. Under the hood ## A multi-agent pipeline will run every test Specialised agents hand off from intent to a verified, reported result — with self-healing along the way. 1 ### Intake Parses your test intent and app context. 2 ### Plan & Prepare A manager agent sequences steps and preps the device session. 3 ### Locate The vision locator pipeline finds each on-screen element. 4 ### Execute Actions run on the device, self-healing on UI drift. 5 ### Verify & Report Assertions + OCR validate results and a rich report is built. Why teams switch ## Solve testing challenges From manual to fully automated — close the gaps that let bugs reach production. ### Comprehensive Toolset Tools for manual, automated, and exploratory testing to cover all bases. ### Scalable Efforts Scale testing across your organization with flexible user roles and permissions. ### CI/CD Integration Integrate seamlessly with CI/CD pipelines for continuous, efficient testing. ### Reduced Bug Leakage Improve testing effectiveness and reduce bug leakage to production by 35%. Take the next step ## Get started with QA Everest Unlock faster, more efficient and reliable testing for your projects today. ### Request a Demo See the platform in action and understand its full potential. ### Start a Free Trial Experience the benefits firsthand with no commitment. ### Contact Sales Discuss pricing and custom enterprise solutions for your organization. ### Achieve More Unlock faster, more efficient, and reliable testing for your projects today. Smoke suite · Android Passed Regression · iOS Running Autonomous crawl Scheduled ## See It In Action Describe a mobile scenario and preview the Device Setup → Gherkin test → device run flow. Your mobile scenario Preview Device Run Your device run will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # User Story Builder & Refinement | QAEverest URL: https://qaeverest.ai/storycreation Summary: Turn vague business ideas into precise, testable user stories. Clarify requirements, align stakeholders, and export in Gherkin, INVEST or traditional formats. Skip to main content AI User-Story Refinement # User Story Builder & Refinement Convert unclear business ideas into precise, actionable user stories that are easy to understand, implement, and test—bridging the gap between stakeholders and development teams. ## It catches what humans miss Generated acceptance criteria, automatically checked for gaps and conflicts — with a confidence score on every estimate. - A reset link is emailed within 60 seconds of the request. - The reset link expires after 30 minutes. - The new password must meet complexity rules. 1 gap — no rate-limit criterion 1 conflict — expiry vs. resend window Priority: High · 92% sure Story points: 5 · 85% sure Sprint 14 · 78% sure ## How Our Story Refinement Works 1 ### Input Your Raw Ideas Enter your initial thoughts, vague requirements, or incomplete user stories. 2 ### AI Analysis & Suggestions Our system identifies missing elements and suggests improvements. 3 ### Collaborative Refinement Loop in stakeholders by email and watch their answers arrive in real time to sharpen the story. 4 ### Export or Generate Test Cases Push the refined story into CI/CD, or generate AI test cases from it in one click. ## Powerful by Design Story Quality Score 88% Clarity Testability Completeness Business Value As a registered user, I want to reset my password, So that I can regain access when I forget it. - ### Clarify Vague Ideas with AI Turn casual, incomplete requirements into clear, properly structured stories with ready-made acceptance criteria. - ### Story Quality Health Score Every story is scored on clarity, testability, completeness and business value — so you know it's sprint-ready. - ### Real-Time Stakeholder Collaboration Email clarifying questions to stakeholders and get notified the instant they reply — answers merge into your story automatically. - ### Import from Jira, Confluence, Git, Figma & Files Pull issues and pages, or upload PDF/DOCX/TXT/Markdown/images — AI extracts a clean story to refine. - ### Test-Ready in One Click See an estimated test-case count, then generate AI test cases or export to CI/CD instantly. ## See It In Action Type a raw story or idea below and preview the Story Details → Questionnaire → Refined Story flow. Your raw story / idea Refine Sample Story Your refined user story will appear here. ## Ready to Transform Your User Stories? Start creating clear, actionable requirements today. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Performance & Security Testing with OWASP Scans | QAEverest URL: https://qaeverest.ai/performancesecurity Summary: Load, stress, spike and soak testing with p95/p99 latency, throughput and breaking-point reporting — plus header, SSL/TLS and vulnerability scans, each returned with an AI verdict. Skip to main content Performance × Security # Elevate Your Performance & Security Load, stress, spike and soak testing plus OWASP-aligned security scans — each run measured, stored and returned with an AI verdict. Get a Quote Explore capabilities 4 Load profiles tested p99 Latency percentiles reported OWASP Top-10 aligned scans 6 Vulnerability probes Performance ## End-to-End Performance Assurance Measured before launch and re-measured on every release — so a regression surfaces in your pipeline, not in production. PRE-RELEASE ### Pre-Go-Live Performance Comprehensive performance testing and analysis before your application goes live. We identify bottlenecks and ensure optimal functionality under anticipated loads. API / CI ### Repeatable Runs in Your Pipeline Trigger the same load, stress, spike or soak run from your CI pipeline through our public API. Every run is stored against the project, so each release is compared with the last instead of tested once and forgotten. Risk ## Mitigating Performance Risks We surface failure modes early — across every load profile your users can throw at you. Load Stress Spike Soak Scalability Breaking Point ### Undetected Issues Unidentified performance issues can lead to system failures, poor user experience, and significant financial losses. We proactively identify and address these risks. ### Load & Scalability Load and scalability profiles measure response-time percentiles, throughput in requests per second, and error rate as concurrency climbs — showing how much traffic your application absorbs before quality degrades. ### Breaking Point Analysis Stress runs step the load up until failures appear, reporting the concurrency level at which error rates crossed your threshold and the last level that held steady — your usable capacity ceiling. Reporting ## What Every Run Reports Each test returns the same measured set, stored against your project — so you can tell whether a release got slower, not just whether it passed. p95 / p99 latency Average · min · max Throughput (req/sec) Error rate % Status-code breakdown Bytes transferred ### Latency You Can Act On Every run reports p95 and p99 alongside average, minimum and maximum response times — so a slow tail shows up instead of hiding behind a healthy-looking average. ### Capacity and Drift Stress runs return the concurrency level where errors crossed your threshold, and the last level that held steady. Soak runs segment the full duration so gradual slow-down becomes visible. ### An AI Verdict on Every Run Results are analysed automatically into a pass, warning or fail verdict — with a plain-English summary, the findings behind it, and recommended next steps. AI Security ## AI-Assisted Security Scanning Automated probes across headers, transport and injection — read back to you as a verdict, not a raw dump. Security headers SSL / TLS config Injection & XSS Sensitive file exposure Open redirect Directory listing AI ### Four Scan Types, One Run Header, SSL/TLS, vulnerability and full-audit scans against any URL — with findings graded critical through low and rolled up into an overall risk level. AI ### Fewer False Positives Probes are baseline-aware: a single-page app that returns its shell for every path, or a page that simply mentions a database name, no longer trips a finding the way naive scanners do. AI ### AI-Written Analysis Every completed scan is analysed into a pass, warning or critical verdict with a plain-English summary — so a raw findings list becomes something you can hand to a developer. AI ### Prioritised Remediation Alongside the verdict you get the specific findings that drove it and recommended fixes, ordered so the highest-severity issues are addressed first. Roadmap ## What We’re Building Next These are in development, not yet available. Everything described elsewhere on this page ships today — we’d rather show you the roadmap than blur the line. IN DEVELOPMENT ### Scheduled & Continuous Scanning Recurring scans and load runs on a schedule, so drift and newly introduced weaknesses are caught between releases rather than at the next manual run. IN DEVELOPMENT ### Authenticated & Session Testing Probes that log in and exercise protected routes, extending coverage into access control and session handling beyond today’s unauthenticated surface. IN DEVELOPMENT ### Multi-Step Transaction Load Scripted user journeys with values carried between steps, so load can be driven through a full checkout or onboarding flow rather than a single endpoint. ## See It In Action Enter a target URL and preview the load, stress and security runs QAEverest executes against it. Your target URL Preview Sample Runs Your performance and security results will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Accessibility Testing & Inclusive Design | QAEverest URL: https://qaeverest.ai/accessibility Summary: Run WCAG 2.2 & AAA accessibility scans for ARIA, media, forms and navigation, get AI-powered remediation, and ship products that work on every device. Skip to main content Inclusive experiences, by design # Accessibility that includes everyone QAEverest helps you find and fix barriers before they reach your users. Run WCAG 2.2 & AAA scans for ARIA, media, forms and navigation, get AI-powered remediation, and deliver experiences that work for every person — on any device, with or without a mouse. Start Free Book a Demo 98 / 100 Accessibility Score - ARIA & landmarks valid - All images have alt text - 1 minor contrast notice - Fully keyboard operable WCAG 2.2 & AAA AA standards targeted across every scan 0–100 Accessibility score on every audited page 4 Focused scan modes, from ARIA to full audit AI Automated remediation guidance on findings Accessibility Overview ## Inclusive quality, woven into your workflow Accessibility isn’t a separate checklist at QAEverest — it lives inside the same platform you use to create stories, generate test cases and automate testing. Point a scan at any page and the platform inspects structure, media, forms and navigation against WCAG 2.2 & AAA, then turns the results into a clear score and prioritised actions. - Audit any URL across four focused scan modes - AI explains each finding and recommends a fix - Severity grading helps you triage what matters first - Track scan history to watch your score improve Critical Serious Moderate Minor Findings graded by impact, just like in the platform. ## Accessibility Features & Capabilities Real scanning power, paired with guidance your whole team can act on. ### ARIA & Semantics Audit Validates lang, page title, landmark roles, heading hierarchy, tabindex and ARIA attributes so assistive technologies can interpret your structure correctly. ### Images & Media Checks Verifies alt text on images, captions on video and accessible controls on audio — ensuring non-text content has meaningful text alternatives. ### Forms & Navigation Audits form labels, button names, descriptive link text and keyboard navigation paths so every interactive element is operable and clearly identified. ### Full WCAG Audit Combines all checks into one comprehensive WCAG 2.2 & AAA pass, returning a single score, severity breakdown and prioritised list of findings. ### AI-Powered Remediation Each scan is paired with an AI analysis that explains findings in plain language and recommends concrete, code-level fixes — not just a list of errors. ### Scoring & Severity Insight Findings are graded critical, serious, moderate or minor, so teams can triage confidently and track accessibility debt over time. ## Keyboard Navigation Support Every action is reachable without a mouse — a skip-to-content link gets keyboard users to the main content fast. Tab Move forward through every interactive element in a logical order. Shift Tab Step backward through the focus order without using a mouse. Enter Activate links, buttons and primary actions. Esc Dismiss dialogs and modal panels instantly. ## Responsive & Cross-Device Experience Layouts adapt fluidly across the same breakpoints used throughout QAEverest. ### Desktop & Large Screens Spacious layouts tuned for breakpoints above 1196px with comfortable reading widths. ### Tablet & Touch Fluid grids and touch-friendly targets adapt cleanly across the 768–1195px range. ### Mobile First Content reflows to a single, readable column with scaled typography on small screens. ## User Experience Enhancements Thoughtful details that make the platform clearer for everyone. ### Visual Clarity High-contrast text, clear severity colour coding and consistent spacing keep information easy to scan. ### Assistive-Tech Friendly Semantic landmarks, descriptive labels and aria-hidden decorative icons help screen readers stay accurate. ### Skip-to-Content A skip-navigation link lets keyboard users jump straight past the menu to the main content. ### Plain-Language Guidance Results and AI recommendations avoid jargon, making accessibility approachable for the whole team. ## Accessibility Standards & Best Practices Scans are anchored in the four WCAG principles — Perceivable, Operable, Understandable and Robust. P ### Perceivable Information and UI components are presented in ways users can perceive — text alternatives, captions and sufficient contrast. O ### Operable Navigation and controls are fully operable, including complete keyboard access and clear focus order. U ### Understandable Content reads predictably and consistently, with clear labels, instructions and error messaging. R ### Robust Valid semantics and ARIA ensure content stays reliable across browsers and assistive technologies. ## Accessibility Highlights 4 WCAG scan modes ARIA & Semantics, Images & Media, Forms & Navigation, and a Full Audit. 4 Severity levels Critical, serious, moderate and minor — so triage is never guesswork. 100 Point score scale A single, trackable accessibility score for every page you audit. ∞ Re-scans & history Re-run anytime and review past scans to prove your progress. ## See It In Action Enter a page URL, choose a scan like Full Audit, and preview the scored findings QAEverest reports. Your page URL Run Sample Scan Your accessibility findings will appear here. ## Ready to build for everyone? Run your first WCAG 2.2 & AAA scan and turn accessibility findings into shipped fixes. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Exploratory Testing | QAEverest URL: https://qaeverest.ai/exploratory-testing Summary: Give the autonomous exploratory tester a URL — it logs in, crawls the app, generates Gherkin flows, runs them in a real browser and files bugs to Jira or ClickUp. Skip to main content Zero test scripts Real Playwright browser AI-generated Gherkin flows # Point it at a URL. Walk away. The Autonomous Exploratory Tester logs into your application, crawls every page, generates Gherkin test flows using AI, executes each one in a real browser, and triages every failure into a structured bug report — all without a single line of written test input. Start Free Book a Demo Autonomous Exploratory Tester 5 credits Application URL * https://your-application.com Username / Email admin@company.com Password •••••••••• Advanced · Max pages: 8 · Depth: 2 Start Autonomous Run Credentials never stored — used only to drive the run 5 Fully automated phases Login · Crawl · Discover Flows · Execute · Triage Bugs — every step handled by the agent 25 Pages crawled per run Configurable scope from 1 page up to 25, with crawl depth control from 1 to 4 levels 0 Test scripts required Provide a URL and optional credentials — AI generates and executes every test flow How It Works ## Five automated phases. Zero written input. From the moment you hit Launch to the moment bugs appear in Jira or ClickUp, every step is handled by the agent. Phase 01 ### Login The agent opens your application URL in a real Playwright browser and heuristically locates the login form — finding the password field, username/email input, and submit button without any written rules. It fills your credentials, submits the form, and confirms success by checking for a URL change, appearance of a logout link, or disappearance of the password field. Works on public apps with no login form too — the agent detects the absence of a password field and proceeds directly to crawl, no credentials required. Phase 02 ### Crawl Starting from the post-login page, the agent performs a breadth-first crawl of your application, staying strictly within the same origin — it never wanders to external sites. For every page visited it records the title, headings, all links, visible buttons, input fields, and form count, building a complete interactive map of your UI surface. A built-in destructive-action filter prevents the crawler from following logout, delete, cancel-subscription, or similar links — keeping your session alive and your data intact. Phase 03 ### Discover Flows The complete crawl map — page titles, headings, button labels, input field names — is fed to AI, which synthesises up to 6 realistic Gherkin test flows. Scenarios cover navigation sequences, form submissions, search interactions, filtering, and pagination, all referencing real labels from your live application. A deterministic heuristic fallback ensures flows are always generated. Each flow is a standalone Gherkin Feature with one Scenario that starts from a concrete URL and references actual button and field labels discovered during the crawl. Phase 04 ### Execute Every synthesised flow is executed in a real browser through the same PipelineRunner that powers QAEverest's UI Automation module. The browser session starts pre-authenticated — the login state saved during Phase 1 is injected — so every scenario begins exactly where a real user would. Per-flow results include pass/fail status, the failed step, error detail, and a screenshot on failure. The agent launches Chromium, falling back to Firefox if unavailable. Flows run headlessly in the background — no browser window appears on your machine. Phase 05 ### Triage Bugs Each failed flow is analysed by AI, which produces a structured bug candidate: a concise title, a severity rating (Critical, High, Medium, or Low), the expected behaviour, and the actual behaviour. The Gherkin flow itself serves as the built-in reproduction steps. Bug cards appear in the results view instantly, ready to file. Heuristic fallback classifies bugs even without AI connectivity — timeouts and navigation errors are rated High, other failures Medium — so no failure goes unrecorded. What You Get ## Every failure becomes a structured bug report The agent doesn't just flag failures — it writes the bug for you. Each failed flow becomes a complete bug candidate with a title, severity, expected vs actual behaviour, and the Gherkin flow as reproduction steps — ready to file in one click. Critical App crash, login failure, navigation timeout, or 500 error. Fix before any release. High Major feature broken or core workflow blocked. Significant impact on real users. Medium Unexpected behaviour with a workaround available. Schedule for the current sprint. Low Edge-case or minor observation with no workflow impact. Log in backlog. Sample bug card Critical Step failed: Navigate to checkout page Expected Cart page loads with selected items and order total visible Actual Navigation timeout — page did not respond within 30 seconds Flow End-to-end checkout — add item and proceed to payment Steps to reproduce Feature: Checkout flow Scenario: Add item and proceed Given I am on the product listing When I click "Add to cart" And I click "Proceed to checkout" Then I should see the cart summary File to Jira File to ClickUp Compatibility ## Works for public and protected applications No login? No problem. Credentials? The agent finds the form and fills it. ### Public applications If the target URL has no password field, the agent detects this automatically and proceeds directly to crawl — no credentials configuration needed. - E-commerce product catalogues - Public dashboards and portals - Documentation and support sites - Marketing and landing pages ### Protected applications Provide your application username and password. The agent locates the login form, fills it, verifies success, and saves the session state so every executed flow starts pre-authenticated. - SaaS application dashboards - Admin and management panels - Internal tools and intranets - Customer and partner portals Safe by design during crawl The crawler never follows links whose text or URL contains logout, delete, cancel subscription, deactivate, reset, or similar destructive hints — your session stays alive and your data stays untouched throughout the run. ## Your credentials never touch our database At run launch, your username and password are passed to the agent over a secure stdin channel as JSON — they never appear on CLI arguments (which are visible in the process table), never written to disk, and never stored in the database. The run record retains only a masked username such as “Ad***” for display purposes. Stdin-only transfer Never in the process table Never written to disk Never stored in the database Bug Filing ## One click from bug candidate to Jira or ClickUp Every bug card has a File to Jira and a File to ClickUp button. The agent pre-fills the entire report — you only need to provide your project key or list ID. Jira ### File to Jira Provide your Jira project key (e.g. QA ). The agent creates an issue in your configured Jira instance using your API key from Settings. Summary AI-written bug title Severity Critical / High / Medium / Low Description Expected · Actual · Target URL · Flow Steps Gherkin flow as code block Issue type Bug ClickUp ### File to ClickUp Provide your ClickUp list ID. The agent creates a task using the token configured in Settings — personal or organisation-level. Name AI-written bug title Severity Critical / High / Medium / Low Description Expected · Actual · Target URL · Flow Steps Gherkin flow as code block Linked issue URL returned and stored with the run Once filed, the bug card shows the issue key (e.g. QA-42 ) with a direct link to the tracker. Bugs already filed cannot be filed again from the same run. ## See It In Action Enter your application URL and preview the Login → Crawl → Discover Flows → Execute → Triage Bugs run. Your application URL Start Sample Run Your bug candidates will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Requirements Traceability | QAEverest URL: https://qaeverest.ai/requirements-traceability Summary: Link every requirement to the tests that verify it, track live coverage and run state, run AI gap analysis, and get a PR-scoped, risk-ranked test set in seconds. Skip to main content Requirements Traceability Matrix Smart PR Test Selection AI Gap Analysis # Every Requirement, Every Test, Live. The Requirements Traceability Matrix links your refined stories, Jira issues, and generation records to the test cases that cover them — showing live coverage, run state, and business risk in one view. Paste a GitHub PR URL to get the smallest risk-ranked test set for that change in seconds. Start Free Book a Demo 78% Requirements Covered 62% Covered & Passing 3 High-Risk Uncovered Requirement Risk Coverage Last Run Authentication · 2 of 3 covered User login with email and password Refined Story SR-4A2F · 2 AC B0 Covered Passing Password reset via email link Jira QA-17 B1 Uncovered — Dashboard · 1 of 2 covered Display test execution summary Record REC-B31C B2 Covered Failing 4 Live KPI metrics Requirements Covered %, Covered & Passing %, High-Risk Uncovered, Total Requirements — updated on every load B0–B3 Business-risk tiers Critical (B0), High (B1), Medium (B2), Low (B3) — derived from story priority and test-case risk metadata PR Smart test selection Paste a GitHub PR URL — the matrix maps changed files to requirements and returns the smallest risk-ranked test set 2 Credits per AI gap analysis One AI call produces a verdict (critical / warning / pass), top uncovered gaps, and recommended next actions Requirement Sources ## Three kinds of requirements, one unified matrix The matrix pulls requirements from every place QAEverest already stores them — no separate import step needed. SR-4A2F ### Refined Story Every Story Refinement you create becomes a requirement row. Its acceptance criteria appear in the detail drawer, and its priority field drives the B0–B3 risk tier assigned to the row. Acceptance criteria Priority → risk tier Domain · Module REC-B31C ### Generation Record Records created when you generate test cases (UserRecord) are linked automatically. Each record carries its domain, module, story detail, and test-case type — no manual mapping required. Domain · Module Story detail Test-case type QA-17 ### Jira Issue Requirements imported from Jira appear with their issue key as the external reference — displayed alongside the requirement label and used for cross-tool traceability. Any test artifact can be linked to a Jira issue. Jira issue key Issue summary Project context via TraceLink ↓ Test Cases Individual test cases linked directly — each shows its last run status (Passing / Failing / Not run) and business risk in the matrix row. Suites Link an entire suite in one step — all member test cases are resolved automatically, and coverage reflects the suite's worst-case run state. Non-invasive by design Trace links live in their own collection — no foreign keys are added to existing story, record, or test-case schemas. The feature can be toggled off with zero data migration. Smart Test Selection ## Paste a GitHub PR. Get the exact tests to run. The Smart Selection tab fetches a GitHub PR's changed files, maps them to your requirement matrix, and returns the smallest risk-ranked test set that covers the change — ranked failing and unknown first. Unmapped files are flagged so you know exactly where your matrix has blind spots. 1 Paste PR URL GitHub pull request link — any repo with a connected token → 2 AI Maps Changes Changed files are matched to requirements in your matrix via heuristic analysis → 3 Ranked Test Set Smallest set covering the PR's risk, ranked by severity — ready to run github.com / myorg / app / pull / 241 Analyze PR ⚠ 3 requirements touched · 1 coverage gap detected myorg / app #241 · 7 changed files · 3 requirements touched 9 tests to run 94% risk covered ~4m vs ~28m full 1 gap found ⚠ 1 changed requirement has no tests B1 Password reset via email link Generate Recommended tests (9) B0 User login with email and password Passing ▶ Run B1 OAuth token refresh on expiry Not run ▶ Run B2 Session timeout after inactivity Not run ▶ Run Tests to run The smallest set of test cases whose requirements are touched by the PR's changed files, ranked by business risk Risk coverage % Percentage of the touched requirement risk that the recommended test set covers Estimated time Rough estimate in minutes for the selected set vs. the full project suite Coverage gaps Changed requirements with zero linked tests — with a one-click Generate shortcut to fill the gap immediately ### Run tests directly from the suite Each recommended test that belongs to a suite has a Run shortcut. Clicking it navigates straight to that suite's history page so you can initiate execution and review results without searching manually. ### PR analysis history The last 10 PR analyses are stored locally. One click reloads any previous result — useful for comparing how coverage changes across multiple PRs targeting the same module. ### Unmapped file callout Files the PR changed that couldn't be matched to any requirement are listed separately — making coverage blind spots explicit rather than silently ignored. ## See It In Action Paste a GitHub PR URL and preview the risk-ranked test set Smart Selection recommends — 4 tests covering 92% of the risk. Your GitHub PR URL Analyze Sample PR Your recommended test set will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Marketplace & Integrations | QAEverest URL: https://qaeverest.ai/marketplace Summary: Install QAEverest from npm, the VS Code and JetBrains marketplaces, the Claude Code plugin catalog, Atlassian Jira or GitHub. Free to install, connected by a device code, billed to your own account. Skip to main content npm & MCP VS Code & JetBrains Claude Code plugin Atlassian Forge # One QA engine, six places to install it. QAEverest ships as an MCP server and wraps it in a listing for every place you already work — the npm registry, the VS Code and JetBrains marketplaces, the Claude Code plugin catalog, Atlassian Jira and GitHub. Each one is free to install, connects with a short device code instead of a pasted key, and bills straight to your own QAEverest account. Start Free QAEverest — AI Test Case Generation qaeverest · v1.5.4 · MIT Install AI Testing MCP Free to install $ npm install -g qaeverest-mcp copy Tools registered on connect generate_testcases 1 credit generate_api_tests 2 credits security_scan 10 credits usage free Billed to your own QAEverest account — no marketplace revenue share 6 Distribution channels npm, VS Code, JetBrains, Claude Code, Atlassian Jira and GitHub — every listing installs the same QAEverest engine 7 Tools exposed over MCP Test-case, API, UI and mobile generation plus security scans, performance tests and a free usage check 1000+ Free credits on signup No card required — roughly 100 test-case generations before you pay for anything 0% Marketplace revenue cut Every listing is free to install and bills to your own QAEverest account — bring-your-own-key, no middleman Where QAEverest Is Listed ## Pick the marketplace you already open The listings differ only in how they sign you in. Behind every one of them sits the same qaeverest-mcp server calling the same public API, so a suite generated in Cursor is the same suite your teammate sees in Jira. v1.1.0 ### npm — MCP server qaeverest-mcp The stdio MCP server every other listing wraps. Install it globally and register it in any MCP-compatible client — Claude Code, Cursor, Windsurf — to expose the full QAEverest tool set to your agent. npm install -g qaeverest-mcp Node 18+ stdio transport MIT licence v1.5.4 ### VS Code Marketplace qaeverest.qaeverest-mcp “QAEverest — AI Test Case Generation” registers the MCP server for Copilot Chat and any MCP-capable agent in the editor, so test generation happens beside the code it covers. ext install qaeverest.qaeverest-mcp AI · Testing Copilot Chat VS Code 1.101+ v0.1.1 ### JetBrains Marketplace ai.qaeverest.jetbrains A deliberately thin onboarding plugin: Tools → QAEverest → Sign In runs the device flow, stores your key in the IDE PasswordSafe — never plain settings — and hands off to AI Assistant’s MCP client. Tools → QAEverest → Sign In IntelliJ 2025.1+ PasswordSafe AI Assistant v1.1.0 ### Claude Code plugin qaeverest A plugin catalog you can install from directly, wrapping qaeverest-mcp through .mcp.json and adding first-class slash commands to the Claude Code session. /qaeverest:generate-tests /qaeverest:security-scan /qaeverest:usage self-hosted catalog Forge · nodejs22 ### Atlassian — Jira Forge app QAEverest Test Generator An issue panel and issue-context module that reads only the summary and description of the issue you open it on, generates test cases, and pushes the ones you approve back as sub-tasks. read:jira-work · write:jira-work Issue panel Sub-task push Nothing written automatically Marketplace listing ### GitHub PR-native QA bot Bring QAEverest into the pull request itself — branch and PR diffs import as ready-to-test stories, and the QA bot comments risk analysis on the changes under review. Branch / PR → story → tests PR risk comments Diff import CI/CD triggers Connecting ## No key to copy, no config file to hand-edit Every extension uses the same device-authorization flow. You never see a credential in your clipboard, and the key that reaches your editor is minted fresh for that device. 1 Run “Sign in to QAEverest” The extension calls /api/v1/device/start and shows a short user code such as WDJB-MJHT → 2 Approve it in the browser Your browser opens app.qaeverest.ai/device already carrying the code — sign in or create a free account and approve → 3 The key lands in the IDE A fresh qae_ key is minted, returned exactly once to the polling client, and stored in the IDE’s secure credential store QAEverest: Sign In waiting… Enter this code to connect your IDE W D J B – M J H T app.qaeverest.ai / device POST /api/v1/device/poll authorization_pending POST /api/v1/device/poll authorization_pending POST /api/v1/device/poll approved · key issued Returned exactly once, then stored in the IDE credential store — never pasted, never logged. Two credentials, two jobs A qae_ key (or a v1 JWT exchanged from it) signs service calls. A separate portal token, valid for eight hours, handles account work — regenerating keys, billing and approving devices. Stored as a hash, shown once QAEverest keeps only a one-way hash of your key. The raw value is displayed at generation and never again — rotating a key kills the old one on the next request. Manual setup still works Prefer to wire it yourself? Create an account, copy your key and set QAEVEREST_API_KEY in the environment — the same variable every listing reads. Revocable per developer Issue a key per teammate under one account, then label, expire or revoke any of them independently without disturbing the rest. What Your Agent Gets ## Seven tools, one catalogue, priced per call Once the server is registered, these arrive in your agent’s tool list. Cost is metered per unit against your credit balance and written to your usage log — checking the balance itself is free. Tool What it does Credits / call generate_testcases User story → functional, negative, edge and accessibility test cases 1 generate_api_tests REST story or spec → an API test suite 2 generate_ui_tests UI test generation, returned as an async job you poll 5 generate_mobile_tests Mobile test generation, returned as an async job you poll 5 security_scan OWASP, security-header and SSL/TLS scan of a URL 10 performance_test Load, stress, spike or soak test of an endpoint 10 usage Remaining credits, request quota and current per-call rates free Which tools appear is up to your account Test-case generation, API, UI and mobile automation, security and performance scanning are toggled per account. A tool your plan doesn’t cover returns a clear “service not enabled” response rather than failing silently. Bring Your Own Account ## Free to install, billed where you can see it Nothing is charged through a marketplace. Usage lands on the QAEverest account you control, so the same balance covers the web app, the IDE and the API no matter which listing spent it. Free tier Free credits A starter credit allocation granted the moment you sign up, with a request allowance and all six services switched on. No card, no trial clock. Pay as you go Top-up packs Buy credits in 1,000, 5,000 or 20,000 packs whenever you need them. Purchase intent is stored server-side and the granted amount is read back from the database on verify. Plans Monthly · quarterly · annual Included credits plus higher rate limits, managed from the same API dashboard that shows your usage log and remaining balance. Enterprise Talk to us SSO, invoicing and on-premises deployment under separately agreed commercial terms — the whole stack inside your own network. ## See It In Action Type a user story the way you would inside your editor and preview what the MCP tool call hands back to your agent — before you install anything. Your user story, as you'd type it in the IDE Preview Tool Call The tool call and its response will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # OpenAPI & Swagger Testing | QAEverest URL: https://qaeverest.ai/openapi Summary: Turn an OpenAPI or Swagger document into per-operation test coverage — status, schema, negative and auth cases chained into a runnable suite — and drive it all through QAEverest Skip to main content OpenAPI & Swagger import Per-operation coverage A REST API of our own # Your spec already says what should be true. An OpenAPI document is a contract nobody tests. QAEverest reads yours — file or live URL — and turns every path, method, schema and security scheme into the cases that prove it holds: status, shape, negatives and auth, chained into a suite you can run per environment. The same capability is available the other way round, over a documented REST API of our own. Start Free openapi.yaml 3 paths · 7 operations Import openapi : 3.0.3 paths : /orders : get : # list orders post : # create an order /orders/{id} : get , delete generated coverage GET /orders 4 cases 200 schema 401 POST /orders 6 cases 201 required 422 GET /orders/{id} 5 cases 200 404 schema DELETE /orders/{id} 4 cases 204 403 idempotent Chained into one suite — the id created by POST feeds the GET and DELETE that follow it. 2 Ways to bring a spec in Upload the OpenAPI or Swagger document as a file, or point QAEverest at a live spec URL behind your own auth header 1 : n Operation to coverage Every path and method in the document becomes its own set of positive, negative and schema-validation cases 16 Endpoints on our own API Generation, scanning, suite building, execution and reporting — the same contract our IDE plugins call qae_ One key, every service Sent as x-api-key or a Bearer token, metered per unit against your balance and written to your usage log Spec To Coverage ## Four steps between a document and a green run Importing is deliberately boring — the interesting part is what the parser pulls out of the document and how little you have to restate by hand. 1 Import Drop in a Swagger or OpenAPI document, or hand over the URL your service already publishes it at → 2 Parse Paths, methods, parameters, request and response schemas and declared auth schemes are read out of the document → 3 Generate Each operation turns into cases — happy path, negatives, edge values and response-shape assertions → 4 Execute Promote the cases into a chained suite and run it per environment with injected data and stored variables ### What the parser reads Paths and operations Every path item and each method under it becomes a separately addressable unit of coverage — nothing is collapsed into a single “API” test. Request and response schemas Typed bodies give the generator the required fields, formats and enums it needs to write both the valid call and the ones that should be rejected. Declared security schemes The auth the spec declares becomes test material of its own: authorised calls, missing credentials and credentials that shouldn’t be enough. Parameters and examples Path, query and header parameters — plus any examples the document carries — seed the data used across the generated suite. ### Accepted inputs OpenAPI document JSON or YAML, uploaded as a file Swagger document The same import path — older specs are not a special case Live spec URL Point at the endpoint your service already publishes Postman collection For teams whose contract lives in a collection instead Organisation admins control whether spec import is available to their users, so an enterprise account can keep the surface closed until it chooses to open it. Per Operation ## Six things every operation gets tested for A spec import that produces one smoke test per endpoint isn’t coverage. These run for each documented operation, from both sides of the contract. ### Status-code assertions The documented success response becomes the baseline assertion for the operation, so a silently changed status code fails the run rather than passing quietly. ### Response-schema validation Payloads are checked against the shape the spec promises — a dropped field or a string where a number was declared is a failure, not a warning. ### Negative and edge cases Required fields are omitted, enums are violated and boundary values are pushed, so the contract is exercised from the wrong side as well as the right one. ### Auth and permission paths Calls run without credentials and with the wrong ones, confirming that protected operations actually reject what the spec says they should. ### Chained data injection A value returned by one call feeds the next, so create-then-read-then-delete flows run as one suite instead of four disconnected requests. ### Environment-scoped runs The same generated suite runs against Dev, QA and Prod by swapping the base URL and stored variables — the spec is imported once. The Other Direction ## QAEverest is itself an API you can call Everything above is reachable programmatically. Our IDE plugins, the MCP server and the Jira app are all clients of the same public v1 surface — there is no privileged internal path they use and you don’t. Endpoint Purpose Credits POST /api/v1/auth/token Exchange a qae_ key for a short-lived access token — GET /api/v1/whoami Identify the presented key and what it is allowed to do — GET /api/v1/usage Credits remaining, request quota and current per-call rates free POST /api/v1/generate-testcases Story text → structured test cases 1 POST /api/v1/api-tests/generate Story or spec → an API test suite 2 POST /api/v1/ui-tests Start a UI generation job 5 GET /api/v1/ui-tests/{jobId} Poll that UI job until it completes — POST /api/v1/mobile-tests Start a mobile generation job 5 GET /api/v1/mobile-tests/{jobId} Poll that mobile job until it completes — POST /api/v1/security-scan Header, SSL/TLS and vulnerability scan of a URL 10 POST /api/v1/performance-test Load, stress, spike or soak run 10 POST /api/v1/suites/build Document or requirement text → a runnable suite in one call metered GET /api/v1/suites List your automation suites with id, title, type and count — POST /api/v1/execute Start a run — returns 202 while it continues server-side metered GET /api/v1/execute/results Latest pass/fail outcome for a suite — GET /api/v1/execute/report Fetch the run report for a completed execution — Endpoints marked — don’t consume credits. Generation and scanning are metered per unit at the rates set on your account, and metered means the charge depends on what the call ends up producing. Every call is written to your usage log either way. ### Authentication Send your key on every request in whichever form your client prefers. x-api-key: qae_•••••••••••• # or, equivalently Authorization: Bearer qae_•••••••••••• Clients that would rather not hold the key long-term can exchange it at POST /api/v1/auth/token for a short-lived access token — the route the MCP server takes. QAEverest stores only a hash of the key itself; the raw value is shown once, at generation. GET /api/v1/whoami is the one endpoint that accepts both an account key and a personal key — clients call it on connect to learn what the presented credential may actually do. ### Sync, async and 202 sync Test-case and API-test generation, security scans and performance runs answer in the response body. job UI and mobile generation return a job id — poll /ui-tests/{jobId} or /mobile-tests/{jobId} until it resolves. 202 Execution returns immediately while the run continues server-side; read /execute/results for the outcome. POST /api/v1/suites/build runs the AI engine twice — generate, then step-generate — so give that one a long client timeout. ### One error shape, five things it tells you Failures come back as { success: false, error: "…" } with a status that says which of your problems it is — never a bare 500 you have to guess at. 400 Bad request A required field is missing — the message names it, e.g. “suite_id” or “suite_title” is required. 401 Unauthorised The key is absent, unrecognised, expired or revoked. Revocation takes effect on the next request. 402 Insufficient credits The response states the per-unit rate for the service and the balance you actually have left. 403 Service not enabled That capability isn’t switched on for your account — a deliberate, readable refusal rather than a silent empty result. 429 Quota reached The monthly request limit for your plan is exhausted; it resets on the next billing cycle. ## See It In Action Point us at an OpenAPI or Swagger document and preview the per-operation coverage QAEverest writes out of it — before a single case is generated against your own account. Your OpenAPI / Swagger document URL Preview Coverage The coverage generated from your spec will appear here. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs Katalon (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-katalon Summary: An honest comparison of QAEverest and Katalon for AI test automation — where each is strong, what each publishes, and which teams should pick which. Sourced from Katalon Skip to main content Comparison # QAEverest vs Katalon Katalon is one of the most established test automation platforms on the market, with a large community, a certification programme and a mature IDE. QAEverest starts a step earlier — at the user story — and carries that story through generation, execution, traceability and reporting in one system. Try QAEverest free Book a demo Katalon details checked against their published material on 29 July 2026. ## The short answer ### Choose Katalon if - Your team already writes Groovy or Java test scripts and wants an IDE built around that workflow. - You need a large public community, formal certification and years of third-party tutorials to onboard a big team. - You want a desktop studio your engineers install and own, rather than a browser-first platform. ### Choose QAEverest if - Your tests should start from a Jira story or a Figma frame rather than from a recorded click path. - You want requirements traceability and coverage-gap analysis in the same tool that runs the tests, not in a spreadsheet next to it. - You want functional, API, mobile, performance, security, accessibility and exploratory testing under one story model instead of separate products. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and Katalon Capability QAEverest Katalon Test creation from a written user story Katalon documents AI-assisted authoring; QAEverest's primary input is the story itself, including Jira, GitHub branch diffs and Figma frames. Yes Partial Story refinement before generation Ambiguous acceptance criteria are flagged and rewritten before any test is produced. Yes Not published Web UI automation Yes Yes API automation Yes Yes Mobile and desktop automation Yes Yes Performance testing Katalon documents performance capability; QAEverest derives load profiles from the same user journeys as the functional suite. Yes Partial Security testing Yes Not published Accessibility testing (WCAG) Yes Yes Visual regression Yes Yes Autonomous exploratory agent Give QAEverest a URL and credentials and it maps the application itself, with no scripted path. Yes Not published Requirements traceability matrix Every requirement mapped to the tests covering it, with the gaps ranked by business risk. Yes Not published Coverage-gap analysis Yes Not published Business risk scoring per change Yes Not published Desktop IDE for scripted tests Katalon Studio is a genuine advantage for teams who want to live in an installed IDE. QAEverest is browser-first. Not published Yes Public certification programme Katalon Academy is a real onboarding asset for large teams. Not published Yes SSO (SAML 2.0), RBAC and audit logs Yes Yes Free tier without a sales call Yes Yes ## What Katalon does well Katalon has been in this market long enough to have solved the unglamorous problems: a stable desktop studio, a deep body of documentation, an academy with certification, and an active community forum where an unusual error usually already has an answer. For a large QA organisation that scripts in Groovy or Java and needs dozens of engineers productive quickly, that ecosystem is worth a great deal, and no amount of AI compensates for its absence. ## Where QAEverest is different ### The story is the source of truth Katalon's workflow generally begins with a recorded or hand-written test. QAEverest begins with the requirement — a Jira ticket, a branch diff, a Figma frame — refines it where it is ambiguous, and keeps every generated case traceable back to a specific acceptance criterion. ### Coverage you don't have, not coverage you do A pass-rate percentage tells you the tests you wrote are passing. QAEverest's traceability matrix tells you which requirements nothing covers at all, ranked by business risk — which is the number that should gate a release. ### One platform, one story model Functional, API, mobile, performance, security, accessibility and exploratory testing all run off the same story and report into the same coverage view, rather than living in separate products that meet in a spreadsheet. ## Questions Is QAEverest a drop-in replacement for Katalon? Not identically. If your team's workflow is built around Katalon Studio as an installed IDE with Groovy scripting, QAEverest is a different shape of tool — browser-first, story-first. Teams that adopt QAEverest usually do so because they want requirements traceability and story-driven generation, not because they want a faster version of the same workflow. Can we run both? Yes, and several teams do during evaluation. Because QAEverest generates test cases from requirements, it is often used to find coverage gaps in an existing Katalon suite before anything is migrated. How current is this comparison? Every Katalon row on this page was checked against Katalon's own public documentation on 29 July 2026. Both products ship frequently. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Found something inaccurate about Katalon on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs Functionize (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-functionize Summary: How QAEverest and Functionize compare on AI test automation — agentic execution, enterprise focus, traceability and coverage. Sourced from Functionize Skip to main content Comparison # QAEverest vs Functionize Functionize sells an enterprise-grade agentic testing platform with a strong architecture story and analyst recognition. QAEverest overlaps on AI-driven authoring and execution, and diverges on what happens around the tests: requirements traceability, coverage gaps and a self-serve path that does not begin with a sales call. Try QAEverest free Book a demo Functionize details checked against their published material on 29 July 2026. ## The short answer ### Choose Functionize if - You are procuring for a large enterprise where analyst coverage and a formal architecture review are part of the buying process. - You want a vendor organised around named industry verticals with corresponding reference material. - Your evaluation is led by an enterprise architecture team rather than by the QA engineers who will use the tool day to day. ### Choose QAEverest if - You want to try the product on your own stories today without booking a call. - Traceability from requirement to test to result matters as much to you as the authoring itself. - You need performance, security, accessibility and exploratory testing in the same platform, not as separate purchases. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and Functionize Capability QAEverest Functionize AI-generated test cases Yes Yes Generation from a Jira story or branch diff Functionize documents AI authoring; QAEverest treats the requirement itself as the input and keeps the link. Yes Partial Story refinement before generation Yes Not published Web UI automation Yes Yes API automation Yes Yes Mobile and desktop automation Yes Partial Performance testing Yes Not published Security testing Yes Not published Accessibility testing (WCAG) Yes Not published Autonomous exploratory agent Functionize documents autonomous agent tasks; the scope published differs from a credentialed crawl-and-generate run. Yes Partial Requirements traceability matrix Yes Not published Coverage-gap analysis Yes Not published Business risk scoring per change Yes Not published Published industry vertical material Functionize maintains dedicated healthcare, insurance, finance and SaaS resources. Partial Yes Third-party analyst coverage Functionize references a Gartner market guide. We have none, and say so. Not published Yes Self-serve free tier, no sales call Yes Partial SSO (SAML 2.0), RBAC and audit logs Yes Yes ## What Functionize does well Functionize has built the thing enterprise buyers actually ask for: a coherent architecture narrative, analyst recognition, industry-specific reference material, and content authored under a named founder rather than a faceless team account. In a procurement process where a testing tool must survive review by people who will never open it, those assets carry real weight — and we would rather say so plainly than pretend the only axis that matters is the feature grid below. ## Where QAEverest is different ### You can evaluate it this afternoon Bring ten stories from your backlog, generate against them and judge the output yourself. No call, no credit card. We think a tool in this category should be judged on its output rather than on its deck. ### Traceability is part of the product Every generated case links back to the acceptance criterion that justifies it, and the matrix surfaces the requirements nothing covers. That is a different question from whether the tests you have are passing. ### The full testing surface, one subscription Performance, security, accessibility and exploratory testing run off the same story model as the functional suite, and roll into the same coverage report. ## Questions Functionize has analyst coverage and you don't. Why should we take you seriously? Fair question, and we are not going to argue our way around it. Our answer is to publish the method behind our numbers and let you run the product on your own backlog before you pay anything. Judge the output, not the endorsement. Are you enterprise-ready? SAML 2.0 SSO, role-based access with granular permissions, audit logging and per-organisation tenant isolation are all live. Your test data is encrypted and is not used to train models. How current is this comparison? Every Functionize row was checked against Functionize's own public material on 29 July 2026. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Found something inaccurate about Functionize on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs TestMuai (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-testmuai Summary: QAEverest and TestMuai solve different halves of the same problem — authoring and coverage versus execution scale. A factual comparison sourced from TestMuai Skip to main content Comparison # QAEverest vs TestMuai TestMuai is, first and foremost, execution infrastructure: a very large browser and device cloud with orchestration, plus an AI assistant layered on top. QAEverest is an authoring, coverage and maintenance platform. These are more complementary than most comparison pages admit, and this one says so. Try QAEverest free Book a demo TestMuai details checked against their published material on 29 July 2026. ## The short answer ### Choose TestMuai if - Your bottleneck is execution scale — thousands of parallel sessions across a very wide browser and OS matrix. - You already have a large hand-written Selenium, Playwright, Cypress or Puppeteer suite and simply need somewhere to run it fast. - Cross-browser coverage breadth is the specific problem you are buying to solve. ### Choose QAEverest if - Your bottleneck is writing and maintaining the tests, not running them. - You need to know which requirements have no test at all, not just which tests passed. - You want functional, API, mobile, performance, security, accessibility and exploratory testing generated from one story model. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and TestMuai Capability QAEverest TestMuai Test generation from a written user story TestMuai publishes natural-language test authoring in its AI assistant; QAEverest's input is the requirement itself, with the trace preserved. Yes Partial Story refinement before generation Yes Not published Very large cross-browser and device cloud This is TestMuai's core strength and we will not pretend to match its scale. QAEverest runs across major browsers and connects to device farms. Partial Yes Large-scale parallel orchestration Partial Yes Runs existing Selenium / Playwright / Cypress suites Partial Yes API automation Yes Yes Mobile automation Yes Yes Performance testing Yes Yes Security testing Yes Not published Accessibility testing (WCAG) Yes Yes Visual regression Yes Yes Flake detection and quarantine Yes Yes Autonomous exploratory agent Yes Not published Requirements traceability matrix Yes Not published Coverage-gap analysis Yes Not published Business risk scoring per change Yes Not published SSO (SAML 2.0), RBAC and audit logs Yes Yes Free tier without a sales call Yes Yes ## What TestMuai does well TestMuai operates execution infrastructure at a scale we do not, and it shows: an enormous browser and OS matrix, real devices, heavy parallelism, orchestration that keeps long suites from becoming overnight jobs, and support for essentially every framework a team might already have. If your suite is written and your problem is that it takes four hours to run, that is a TestMuai problem, not a QAEverest one. ## Where QAEverest is different ### We are aimed at the other bottleneck Execution grids make an existing suite faster. They do not tell you which tests should exist. QAEverest generates the suite from your requirements and shows you what is still uncovered. ### Coverage against requirements, not against browsers Running one test on three hundred browsers is breadth in one dimension. Knowing that eleven acceptance criteria have no test at all is breadth in the dimension that ships bugs. ### The whole testing surface from one story Security, accessibility and exploratory runs come from the same story model as the functional tests, and report into the same coverage view. ## Questions Can we use QAEverest and TestMuai together? Yes, and for a lot of teams that is the sensible answer. QAEverest connects to device farms for execution, so you can author and trace with us while running at scale on infrastructure you already pay for. Does QAEverest match TestMuai's browser coverage? No. TestMuai's cloud is larger, and if raw matrix breadth is your buying criterion you should buy TestMuai. We run across every major browser and connect to external device farms where you need more. How current is this comparison? Every TestMuai row was checked against TestMuai's own published material on 29 July 2026. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Found something inaccurate about TestMuai on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs mabl (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-mabl Summary: QAEverest and mabl compared on low-code authoring, auto-healing, coverage and pricing transparency. Every mabl claim sourced from mabl Skip to main content Comparison # QAEverest vs mabl mabl is a low-code, AI-native testing platform with a strong developer-workflow story and GenAI-driven test healing. QAEverest covers similar ground on authoring and healing, and goes further on requirements traceability, security and accessibility — and publishes its pricing. Try QAEverest free Book a demo mabl details checked against their published material on 29 July 2026. ## The short answer ### Choose mabl if - You want a mature low-code editor that non-engineers on your team can drive confidently. - Your tests are already in Postman collections you want to import rather than re-author. - Tight integration into an existing developer workflow — CLI, Jira, coding agents — is the deciding factor. ### Choose QAEverest if - You want tests generated from a written requirement rather than built step by step in an editor. - You need security and accessibility testing in the same platform as your functional suite. - You want to see the price before you talk to anyone. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and mabl Capability QAEverest mabl Low-code / codeless authoring Yes Yes Test generation from a written user story mabl publishes GenAI test generation from a codebase; QAEverest generates from the requirement and keeps the trace to its acceptance criteria. Yes Partial Story refinement before generation Yes Not published Auto-healing of broken tests Both publish AI-driven healing. mabl describes GenAI fixes applied in real time. Yes Yes Web UI automation Yes Yes API automation mabl documents Postman collection import. Yes Yes Mobile automation Yes Yes Performance testing Yes Yes Security testing Yes Not published Accessibility testing (WCAG) Yes Not published Visual regression Yes Partial Autonomous exploratory agent Yes Not published Requirements traceability matrix Yes Not published Coverage-gap analysis Yes Not published Business risk scoring per change Yes Not published Testing of AI application outputs mabl publishes validation of non-deterministic AI app output. We do not offer this today. Not published Yes Published pricing QAEverest lists its plans publicly. We could not find mabl pricing published on its site. Yes Not published SSO (SAML 2.0), RBAC and audit logs Yes Yes ## What mabl does well mabl has a genuinely good low-code editor and an unusually clear point of view about living inside the developer workflow rather than beside it — results pushed to Jira, the CLI and coding agents, tests triggered from early commits through to staging. Its GenAI healing is a real capability rather than a checkbox, and its work on validating non-deterministic AI application output is ahead of where we are. ## Where QAEverest is different ### Generated from the requirement, not assembled in an editor A low-code editor still means a human decides what to test. QAEverest reads the story, refines it where it is ambiguous, and produces the cases — including the negative and permission paths people skip when they are building steps by hand. ### Security and accessibility included WCAG 2.2 accessibility scanning and OWASP-oriented security checks run against the same journeys as your functional suite, rather than being a separate tool and a separate budget line. ### The price is on the website Our plans and credit rates are published. You can work out what QAEverest costs your team without booking a call, which we think should be unremarkable and currently is not. ## Questions Does QAEverest heal broken tests the way mabl does? Both platforms repair tests when the application changes. Our rule is that a repair must preserve the original assertion — anything beyond a safe re-anchor escalates to a human rather than being silently patched, because a healed test that no longer checks anything is worse than a failing one. We use Postman collections. Can QAEverest use them? QAEverest builds API suites from your API definitions and from the story itself. If you have a large existing Postman estate you want lifted as-is, that is a genuine point in mabl's favour and worth weighing. How current is this comparison? Every mabl row was checked against mabl's own site on 29 July 2026. mabl ships quickly. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs Testsigma QAEverest vs BrowserStack Found something inaccurate about mabl on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs Testsigma (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-testsigma Summary: QAEverest and Testsigma compared — both generate tests from plain-English stories, so this page focuses on where they actually diverge: testing surface, coverage analysis, deployment and pricing transparency. Skip to main content Comparison # QAEverest vs Testsigma These two products overlap more than any other pair on this site. Both author tests in plain English, both take user stories, Jira tickets and Figma designs as input, and both aim at QA teams who do not want to write scripts. So rather than manufacture distance, this page concedes what Testsigma does better and concentrates on the two places the products genuinely diverge. Try QAEverest free Book a demo Testsigma details checked against their published material on 29 July 2026. ## The short answer ### Choose Testsigma if - You test Salesforce. Testsigma publishes dedicated support for Lightning, Classic, CPQ, Flows and Experience portals, and nothing here matches that. - You need on-premise or hybrid deployment rather than a hosted platform. - Raw execution scale is a buying criterion — they publish 2,000+ browser and OS combinations and thousands of real devices. ### Choose QAEverest if - You want performance and security testing in the same platform as your functional suite, not as separate purchases. - You need to know which requirements have no test at all, ranked by business risk — not just which tests passed. - You want an exploratory agent that maps the application itself from a URL and credentials. - You want to see the price before speaking to anyone. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and Testsigma Capability QAEverest Testsigma Plain-English / low-code test authoring Yes Yes Generation from a user story, Jira ticket or Figma design Genuinely equivalent on this row — it is the core of both products. Yes Yes Story refinement before generation Ambiguous acceptance criteria are flagged and rewritten before any case is produced. Yes Not published Web UI automation Yes Yes API automation Testsigma documents REST, SOAP and GraphQL plus Postman and OpenAPI import. Yes Yes Mobile automation Yes Yes Desktop automation Yes Yes Accessibility testing Yes Yes Visual regression Yes Yes Self-healing locators Both publish self-healing that updates locators when the UI changes. QAEverest's rule is that a repair must preserve the original assertion — anything beyond a safe re-anchor escalates to a human. Yes Yes Salesforce-specific testing Lightning, Classic, CPQ, Flows, custom objects and Experience portals. A clear win for Testsigma. Not published Yes On-premise / hybrid deployment Not published Yes Very large browser and device cloud QAEverest runs across major browsers and connects to external device farms; Testsigma publishes a far larger owned matrix. Partial Yes Performance testing Yes Not published Security testing Yes Not published Autonomous exploratory agent Yes Not published Requirements traceability matrix Yes Not published Coverage-gap analysis Yes Not published Release confidence / risk scoring Testsigma publishes a release confidence score; QAEverest scores business risk per requirement and per change. Related, not identical — worth evaluating side by side. Yes Partial CI integrations Yes Yes Published pricing QAEverest lists plans and credit rates publicly. Testsigma's site points to a pricing page and an enterprise quote. Yes Not published SSO (SAML 2.0), RBAC and audit logs Yes Yes Free trial Yes Yes ## What Testsigma does well Testsigma has built the same core idea we have — tests written in plain English, generated from the artefacts a team already produces — and in several places has taken it further. Salesforce support is deep and specific in a way generic web automation never is, and Salesforce testing is a genuinely painful problem. On-premise and hybrid deployment opens doors that a hosted-only platform cannot, and the published browser and device matrix is much larger than ours. If you are evaluating both, these are real advantages and you should weigh them rather than discount them because we said so. ## Where QAEverest is different ### A wider testing surface from one story Performance and security testing run off the same user journeys as the functional suite and report into the same coverage view. On Testsigma's published capabilities those are separate concerns; here they are one subscription and one story model. ### Coverage you don't have, not tests you ran The traceability matrix maps every requirement to the tests that verify it and surfaces the ones nothing covers, ranked by business risk. Both products will tell you your suite passed. This is the question that decides whether passing means anything. ### An agent that finds its own way in Give the exploratory agent a URL and credentials and it logs in, crawls, generates flows and runs them without a scripted path — useful precisely where nobody has written the stories yet. ## Questions These products sound very similar. What actually decides it? Two questions. Do you need Salesforce testing or on-premise deployment today — if so, Testsigma. Or do you need performance, security and exploratory testing plus requirement-level coverage analysis in one place — if so, us. On plain-English authoring from stories the two are genuinely comparable, and you should trial both on the same backlog. Does QAEverest heal broken tests the way Testsigma does? Both platforms repair tests when the application changes. Our rule is that a repair must preserve the original assertion — anything beyond a safe re-anchor escalates to a human rather than being silently patched, because a healed test that no longer checks anything is worse than one that fails loudly. How current is this comparison? Every Testsigma row was checked against Testsigma's own site on 29 July 2026. Both products ship quickly. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs BrowserStack Found something inaccurate about Testsigma on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs BrowserStack (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-browserstack Summary: QAEverest and BrowserStack solve different halves of the problem — story-first authoring and requirement coverage versus a 30,000-device execution cloud and a broad test-management suite. A factual comparison sourced from BrowserStack Skip to main content Comparison # QAEverest vs BrowserStack BrowserStack is, first and foremost, execution infrastructure — a 30,000+ real-device cloud with Automate, Percy, Load Testing, Accessibility and a large suite of AI agents on top. QAEverest is an authoring, coverage and maintenance platform that starts a step earlier — at the user story. It reads the requirement, refines it where the acceptance criteria are ambiguous, and generates functional, API, performance, security, accessibility and exploratory tests from that one story model, keeping every case traceable to the criterion that justifies it and surfacing the requirements nothing covers yet. Try QAEverest free Book a demo BrowserStack details checked against their published material on 30 July 2026. ## The short answer ### Choose BrowserStack if - Your bottleneck is execution: you need real devices and browsers at scale, with heavy parallelism, more than you need help writing the tests. - You already have a large hand-written Selenium, Playwright or Cypress suite and want somewhere proven to run it fast. - Percy-grade visual review across a very large device matrix is a hard requirement today rather than a roadmap item. - You want the breadth of BrowserStack's device matrix and 150+ integrations as a single procurement. ### Choose QAEverest if - Your bottleneck is creating and maintaining the tests, not just scaling the infrastructure that runs them — QAEverest generates and executes the suite, then keeps it current as the application changes. - You need to know which requirements have no test at all, ranked by business risk — not just which tests passed. - You want security and performance testing generated from the same story model as your functional suite. - You want an exploratory agent that maps the application itself from a URL and credentials. ## Capability by capability A tick means the vendor documents that capability publicly. “Not published” means we could not find it documented — it is not a claim that the product lacks it. Capability comparison between QAEverest and BrowserStack Capability QAEverest BrowserStack 30,000+ real device and browser cloud This is BrowserStack's core strength and we will not pretend to match its scale. QAEverest runs across major browsers and connects to external device farms. Partial Yes Large-scale parallel execution Partial Yes Runs existing Selenium / Playwright / Cypress suites BrowserStack Automate is built to run suites you already own. Partial Yes Test creation from a written user story BrowserStack publishes an AI Test Case Generator and Low Code Automation; QAEverest's input is the requirement itself, and it keeps every case traceable to a specific acceptance criterion. Yes Partial Story refinement before generation Ambiguous acceptance criteria are flagged and rewritten before any case is produced. Yes Not published Low-code / codeless authoring BrowserStack documents Low Code Automation. Yes Yes Web UI automation Yes Yes API automation BrowserStack publishes API load testing and Requestly for HTTP intercept and mocking; QAEverest builds API suites from your definitions and the story. Yes Partial Mobile automation Yes Yes Performance / load testing BrowserStack publishes browser and API Load Testing; QAEverest derives load profiles from the same user journeys as the functional suite. Yes Yes Security testing Yes Not published Accessibility testing (WCAG) BrowserStack's Accessibility Testing includes screen-reader checks (NVDA, VoiceOver, TalkBack). Yes Yes Visual regression Percy and App Percy are a mature, best-in-class visual product. Yes Yes Self-healing tests Both ship AI-driven self-healing that repairs tests when the UI changes. QAEverest's rule is that a repair must preserve the original assertion — anything beyond a safe re-anchor escalates to a human. Yes Yes Autonomous exploratory agent Give QAEverest a URL and credentials and it logs in, crawls, generates flows and runs them without a scripted path. Yes Not published Requirements traceability BrowserStack Test Management publishes a Traceability Report; QAEverest ties the trace to generation, so a requirement links to the cases produced from it. Yes Partial Coverage-gap analysis The requirements nothing covers at all, surfaced rather than inferred from a pass rate. Yes Not published Business-risk scoring per change BrowserStack publishes a Net Quality Score and Quality Gates; QAEverest scores business risk per requirement and per change. Related, not identical — worth evaluating side by side. Yes Partial Test management and analytics BrowserStack Test Management, Test Management for Jira and Test Reporting & Analytics are a deep, mature suite. Partial Yes CI/CD and project-tool integrations BrowserStack publishes 150+ integrations. Yes Yes SSO (SAML 2.0), RBAC and audit logs Yes Yes Free tier without a sales call Yes Yes ## What BrowserStack does well BrowserStack operates execution infrastructure at real scale: a 30,000-plus real-device cloud, Automate for running the suites teams already have, heavy parallelism and 150+ integrations. On top of that it has built genuine depth — Percy is a best-in-class visual product, its Accessibility Testing does real screen-reader checks, and Load Testing and a mature Test Management suite are shipping. If your problem is that your written suite takes hours to run across every device your users carry, that is exactly the kind of scale BrowserStack is built for. ## Where QAEverest is different ### We are aimed at the other bottleneck An execution cloud makes an existing suite faster and broader. It does not decide which tests should exist. QAEverest reads the requirement, refines it where it is ambiguous, and generates the cases — including the negative and permission paths people skip when building steps by hand. ### Coverage you don't have, not tests you ran BrowserStack will tell you your suite passed, and its Traceability Report maps tests you wrote to requirements. QAEverest's coverage-gap analysis surfaces the requirements nothing covers at all, ranked by business risk — which is the number that should gate a release. ### Security and an agent that finds its own way in OWASP-oriented security checks run against the same journeys as the functional suite, and the exploratory agent logs in from a URL and credentials to map an application nobody has written stories for yet — neither of which we can find BrowserStack publishing. ### One story model, one subscription Functional, API, performance, security, accessibility and exploratory testing all run off the same refined story and roll into the same coverage view, rather than being assembled from separate products. ## Questions Can we use QAEverest and BrowserStack together? Yes, and for a lot of teams that is the sensible answer. QAEverest connects to external device farms for execution, so you can author, refine and trace with us while running at the scale BrowserStack's cloud gives you. Does QAEverest match BrowserStack's device coverage? No. BrowserStack's 30,000-plus real-device cloud is larger than anything we operate, and if raw device and browser breadth is your buying criterion you should buy BrowserStack. We run across every major browser and connect to external device farms where you need more. Does QAEverest heal broken tests the way BrowserStack does? Both platforms repair tests when the application changes. Our rule is that a repair must preserve the original assertion — anything beyond a safe re-anchor escalates to a human rather than being silently patched, because a healed test that no longer checks anything is worse than one that fails loudly. How current is this comparison? Every BrowserStack row on this page was checked against BrowserStack's own public material on 30 July 2026. Both products ship frequently. If a row is out of date, tell us and we will correct it. ## Bring ten stories from your backlog Generate against them and judge the output yourself. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma Found something inaccurate about BrowserStack on this page? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest vs Manual Regression Testing (2026 comparison) | QAEverest URL: https://qaeverest.ai/compare/qaeverest-vs-manual-regression-testing Summary: An honest comparison of automated regression with QAEverest against a manual regression pass — cycle time, effort, coverage, and the work that should stay manual. Skip to main content Comparison # QAEverest vs manual regression testing Manual regression is the most expensive habit in most QA teams — not because testers are slow, but because the same few hundred cases are re-run by hand every release, and the bill arrives again in full every time. This page compares the two cycles honestly, including the work that should stay manual. Try QAEverest free Book a demo This page compares a practice, not a vendor. The effort model below is a worked example with its assumptions stated — not a benchmark and not a customer result. ## The short answer This is not a question of whether machines test better than people. It is a question of which work repeats. ### Keep it manual if - The feature is new and its behaviour is still changing week to week. - You are judging how something feels — usability, tone, visual polish — rather than whether it works. - The test will run once or twice and then never again. - Nothing is written down yet and someone needs to explore the product before anyone can specify it. ### Automate with QAEverest if - The same cases are re-run every release and the result is almost always the same. - Release week is defined by how many hours of manual testing fit before the deadline. - You can report a pass rate but not what the release left untested. - A previous automation attempt died of maintenance and the team is wary of a second. ## One regression cycle, side by side The same release, the same suite of a few hundred cases, run against a build that changed part of the application. - 01 ### Deciding what to re-test Manual Someone reads the release notes and guesses the blast radius. Under time pressure the guess becomes “run everything” or “run the critical path” — neither tied to what actually changed. QAEverest The build's diff is read directly. Changed files map to the requirements they implement, and the requirements map to the cases covering them, so the set to run is derived rather than guessed. - 02 ### Preparing the run Manual Test data is rebuilt by hand, environments are checked, and the sheet from last release is copied into a new tab with the results column cleared. QAEverest Suites carry their own prerequisite steps and environment sets. A run starts from a clean, defined state without anyone rebuilding it. - 03 ### Executing Manual Testers work through the sheet in sequence. Throughput is bounded by how many people you have and how many hours are left before the release. QAEverest Cases run in parallel across browsers and devices, unattended. Throughput is bounded by infrastructure, not by headcount or by the calendar. - 04 ### Triaging failures Manual A failure is a line in a sheet plus whatever the tester remembered to screenshot. Reproducing it often costs as long as finding it did. QAEverest Every step is captured — screenshots, DOM state, network calls, console output — so a failure arrives with its own evidence attached and a root-cause summary. - 05 ### When the UI changes Manual Every affected step in every affected case is edited by hand, across however many documents describe it. QAEverest Locators resolve at run time rather than being frozen at authoring time, and drifted steps are flagged for repair instead of failing silently. - 06 ### Reporting to the release meeting Manual A pass rate over the cases that were run — which says nothing about the cases nobody had time to run. QAEverest Coverage against requirements, with the gaps listed and ranked by business risk. The number that gates the release is what is untested, not what passed. ## Capability by capability The manual column describes what a well-run manual team can do, not a caricature of one — and the judgement work manual testing owns outright has a section of its own further down. Capability comparison between a manual regression team and QAEverest Capability Manual team QAEverest Runs overnight and on every commit No Yes Cost of the tenth run of the same suite This is the single largest structural difference. Manual effort is linear in the number of runs; automated effort is paid once at authoring. Same as the first Near zero Parallel execution across browsers and devices No Yes Consistency between two runs of the same case Varies by tester Identical Test cases generated from a user story or Jira ticket No Yes Test set chosen from what the code diff actually changed No Yes Requirements traceability matrix Spreadsheet, by hand Yes Coverage-gap analysis ranked by business risk No Yes Step-level evidence on every failure Ad hoc screenshots Yes Self-repair when locators drift Manual edit Yes Flaky-test detection and quarantine No Yes API, performance, security and accessibility in the same pass Separate specialists Yes Testing something with no written requirement QAEverest can refine a vague story into testable criteria, but it needs some statement of intent to work from. A tester needs nothing. Yes Partial Onboarding cost for a new joiner Days of shadowing Suite is the documentation Regression suite that survives the person who wrote it Depends on the sheet Yes ## The arithmetic, with the assumptions shown Substitute your own case count and cycle time. The point is not the totals — it is that one column scales with every repeat run and the other does not. Worked effort model for one regression pass Per release Manual pass QAEverest Regression cases in the suite 400 400 Time to execute one full pass ≈ 67 hours ≈ 1.5 hours People needed for a one-day turnaround 9 1 Cost of the second pass that release Another 67 hours Another 1.5 hours Passes you can afford per release Usually one Every build Effort to maintain the suite for a quarter Re-edit the sheet by hand Review flagged steps Assumptions: 400 regression cases; 10 minutes to execute and record one case by hand, including setup and note-taking; a 7-hour effective testing day; automated cases running 20 at a time on shared infrastructure. No allowance is made for the manual triage and re-test of failures, which lands on both columns. These are illustrative figures, not measurements from your environment. ## What manual testing is still better at A comparison page that cannot name these is selling, not comparing. ### The first time anything is tested A brand-new feature with a half-formed spec is a terrible automation candidate and a perfect manual one. Automate it once the behaviour has stopped moving — not before. ### Judgement calls Whether a flow is confusing, whether an error message is condescending, whether a screen is beautiful — no assertion captures these, and pretending otherwise is how teams ship technically-passing products nobody enjoys using. ### Genuine exploration The bug found because a tester wondered what happens if you paste an emoji into the postcode field is a category of bug automation is bad at reaching. Our exploratory agent widens that net; it does not replace the person. ### Short-lived and one-off work A throwaway prototype, a single migration verification, a suite for a product being sunset next quarter — the authoring cost never pays back. Test it by hand. ## Where QAEverest changes the cycle ### The suite is written from the requirement, not from your clicks Point QAEverest at a Jira story, a GitHub branch diff or a Figma frame. Ambiguous acceptance criteria get flagged and refined first, then cases are generated against them — so the suite reflects what the feature is meant to do, not what one person happened to click while recording. ### You run the tests the change actually needs Regression is expensive because teams re-run everything to avoid deciding what matters. QAEverest reads the diff, maps it through the traceability matrix and returns the affected set — a defensible answer to “what do we need to re-run?” instead of a guess. ### Maintenance stops being the reason automation gets abandoned Most manual teams have a dead automation suite in their history — it worked until the UI moved. Locators here resolve at run time, drifted steps are detected and repaired, and flaky cases are quarantined rather than trained into being ignored. ### Your testers move up, not out This is the point of the whole exercise. Automating the four-hundredth repetition of the same login flow does not remove the need for testers; it stops them spending release week on work a machine does better, and returns them to exploratory testing, risk analysis and the judgement calls above. ## How teams actually make the move Nobody automates four hundred cases in a sprint, and the teams that try are the teams whose suite dies. - 1 ### Start with what hurts most Take the twenty cases your team runs every single release without fail. They are the highest-frequency, lowest-judgement work in the suite — the fastest payback in the building. - 2 ### Generate rather than re-type Feed the stories those cases came from into QAEverest instead of transcribing the sheet. Transcription carries every gap in the old suite forward; generation from requirements exposes them. - 3 ### Run both for two releases Keep the manual pass alongside the automated one. Where they disagree, one of the two is wrong — and finding out which is exactly the confidence you need before you retire a manual pass. - 4 ### Give the reclaimed week back to testing The failure mode here is quietly absorbing the saved time into more releases. Spend it on exploratory sessions and coverage gaps, and the suite starts finding bugs the old cycle never could. ## Questions Does QAEverest replace manual testers? No — it replaces the repetitive part of their week. Regression is the work testers least want to do and machines do best: the same steps, the same expected results, over and over. What does not automate is exploratory testing, usability judgement, risk analysis and knowing which part of the product deserves attention this release. Teams that adopt QAEverest generally keep their testers and change what those testers spend release week doing. We tried automation before and the suite died. Why is this different? Most abandoned suites died of maintenance, not of authoring — the UI moved and every locator broke at once. QAEverest resolves locators at run time rather than freezing them when the case was written, flags steps that have drifted so they can be repaired before they fail, and quarantines flaky cases instead of letting the team learn to ignore red. That is the specific failure mode being addressed. How long before we stop running the manual regression pass? Do not stop on day one. Run both in parallel for a couple of releases and compare results — where the two disagree you learn something either way. Most teams retire the manual pass for the high-frequency core first and keep manual attention on new features, which is where it was always most valuable. Our test cases only exist in a spreadsheet. Is that a problem? No, and it is the common starting point. You can import an existing repository or set of cases, but the better result usually comes from generating fresh cases from the stories or requirements those cases were written for — transcribing a sheet carries all of its blind spots forward, while generating from requirements shows you where the sheet was thin. What about the parts of our product with no written requirements? QAEverest can refine a rough description into testable acceptance criteria, so a one-line ticket is usually enough to work from. Where genuinely nothing is written down, that is a manual exploratory job first — and the output of that session becomes the requirement the automated suite is then built against. Is the effort comparison on this page based on our numbers? No. It is a worked example with its assumptions printed next to it — a 400-case suite at ten minutes a case, one tester working a seven-hour day. Substitute your own case count and cycle time; the shape of the result is what matters, which is that manual effort scales with every repeat run and automated effort does not. ## Take the twenty cases you run every release Generate them from the stories they came from, run them against your next build, and compare the result with your manual pass. That is a better evaluation than any comparison table, including this one. Start free — no credit card See pricing ## Other comparisons QAEverest vs Katalon QAEverest vs Testsigma QAEverest vs mabl Selenium alternatives Think we have been unfair to manual testing here? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Playwright alternatives in 2026 | QAEverest URL: https://qaeverest.ai/playwright-alternatives Summary: An honest map of the Playwright alternatives — Cypress, Selenium, WebdriverIO, Appium and AI-driven platforms — what each is genuinely best at, and when Playwright is still the right answer. Skip to main content Alternatives # Playwright alternatives Playwright has become the default recommendation for new browser automation, and deservedly so: auto-waiting, one API across Chromium, Firefox and WebKit, a trace viewer that makes CI failures readable, and bindings in TypeScript, Python, Java and .NET. Teams look past it not because it is weak but because it is a browser library — it emulates mobile rather than driving real devices, it does not touch native apps, and, like every framework here, it still needs someone to write and maintain the tests. Try QAEverest free Book a demo Playwright limitations taken from its own documentation on 29 July 2026, not from third-party round-ups. ## Why teams look for an alternative - Mobile is emulation only. Playwright's device profiles set a viewport, user agent and touch flags in a desktop browser; its own emulation docs are explicit that this is not a real Android or iOS device. - No native mobile apps. Playwright automates web pages, so anything running as a native or hybrid app needs a separate tool regardless. - Real Safari and legacy targets are out of scope. Playwright ships its own WebKit build rather than driving the Safari a user runs, and there is no IE11 or legacy-Edge coverage. - The JavaScript/TypeScript binding leads; the Python, Java and .NET ports trail it on features and community examples, which matters for a polyglot QA team. - It is still a framework, not a testing platform. Selectors break on redesigns, and nothing generates the cases or repairs them for you. ## What Playwright still does better than anything else If your problem is the ergonomics of writing browser automation, Playwright is the answer and we will not pretend otherwise — it is the tool we recommend most often on the rest of these pages, and the one QAEverest exports to by default. Auto-waiting on actionability removes the largest source of flake, browser contexts give real multi-session isolation, network interception and codegen are first-class, and the trace viewer turns a failed CI run into something you can actually debug. For a team that wants to own a fast, modern, in-repo test framework, none of the alternatives below beat it at that job. Most are worth reaching for only when your need sits outside the browser Playwright drives. ## The alternatives, and what each is best at Listed with the trade-off that comes with each. For some teams the right answer on this page is not us, and it is more useful to say so. ### Cypress Open source Best for JavaScript front-end teams who want the best local developer experience in the category — tests running in the app's own loop and a time-travel runner that makes failures obvious in seconds. Trade-off JavaScript only and single-browser-at-a-time by design, per the Cypress docs. It trades Playwright's reach for polish. ### Selenium / WebdriverIO Open source Best for Teams who need a W3C standard, the widest language matrix, or real browser coverage including the actual Safari and older targets Playwright's bundled WebKit does not represent. Trade-off A driver rather than a batteries-included framework: waiting strategy, reporting and parallelism become your problem again. ### Appium Open source Best for The gap Playwright does not cover — real iOS and Android devices, and native and hybrid mobile apps, behind one WebDriver-based API. Trade-off Large setup surface and slower runs; overkill if all you actually need is a mobile-shaped browser viewport. ### QAEverest Commercial platform Best for Teams whose bottleneck is writing and maintaining the tests rather than executing them — cases generated from the requirement, traceable back to it, and exportable as Playwright source you still own. Trade-off A hosted platform, not a library you vendor into your repo. If you want to own the framework layer yourself, Playwright is exactly the right pick. ## Where QAEverest fits ### It starts a step earlier Playwright assumes you already know which tests to write. QAEverest generates the cases from the user story, flags the acceptance criteria too vague to test, and keeps each case traceable to the requirement that justifies it — then exports the suite as Playwright. ### Past the browser boundary, in one platform Playwright emulates mobile and stops at the web. QAEverest runs real mobile on connected device farms, plus API, performance, security and accessibility, all generated from the same story and rolled into one coverage view. ### Coverage, not just execution The traceability matrix shows which requirements have no test at all, ranked by business risk — a question no browser library, Playwright included, answers. ## Questions Is Playwright a good choice in 2026? For writing and running browser tests, it is the one we recommend most. The reasons to add something else are structural rather than quality-related: you need real mobile devices or native apps, the actual Safari or a legacy browser, or you want the tests generated and maintained rather than hand-written. Playwright or QAEverest? If you want to own a fast, in-repo test framework and your team has time to write the tests, Playwright — and QAEverest will export to it so you are not locked in. Choose QAEverest when the problem is the volume of tests to write and maintain, or when you need mobile, API, security and accessibility under one roof rather than the browser alone. Can QAEverest run our existing Playwright tests? QAEverest generates and runs its own suites and exports them as Playwright source, but it is not a drop-in runner for a hand-written Playwright estate. The common pattern is to keep that suite running and use QAEverest to generate coverage for new stories and fill the gaps the traceability matrix surfaces. ## Try it against your own backlog Bring ten stories, generate against them, and compare the output with what your Playwright suite covers today. Keep whichever wins. Start free — no credit card See pricing ## Other alternatives guides Selenium alternatives Cypress alternatives Appium alternatives ## Platform comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Something here out of date about Playwright? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Selenium alternatives in 2026 | QAEverest URL: https://qaeverest.ai/selenium-alternatives Summary: An honest map of the Selenium alternatives — Playwright, Cypress, WebdriverIO, Puppeteer and AI-driven platforms — what each is genuinely best at, and when Selenium is still the right answer. Skip to main content Alternatives # Selenium alternatives Selenium is the reason browser automation is a standard rather than a vendor feature: WebDriver is a W3C specification, and almost every tool below either implements it or was built in reaction to it. Teams look for alternatives less because Selenium is bad and more because it is a driver, not a testing platform — waits, reporting, parallelism, flake handling and maintenance are all left to you. Try QAEverest free Book a demo Selenium limitations taken from its own documentation on 29 July 2026, not from third-party round-ups. ## Why teams look for an alternative - Selenium gives you a driver, not a framework. Waiting strategy, reporting, retries, parallelism and fixtures are all assembled by hand, and every team assembles them slightly differently. - Implicit and explicit waits are the single largest source of flake in most Selenium suites; newer tools auto-wait on actionability by default. - Debugging a failed CI run usually means a screenshot and a stack trace, because trace capture is something you have to build. - Maintenance dominates. Selectors break on every redesign and nothing repairs them for you. ## What Selenium still does better than anything else Selenium is the only option here that is a genuine W3C standard with first-class bindings in Java, Python, C#, Ruby and JavaScript, real browser coverage including Safari and legacy targets, and two decades of accumulated answers to obscure problems. If your organisation has polyglot teams, a compliance requirement for a standards-based stack, or an existing Grid and a large body of working tests, replacing Selenium is usually the wrong project. Most of the alternatives below are worth adopting alongside it, not instead of it. ## The alternatives, and what each is best at Listed with the trade-off that comes with each. For some teams the right answer on this page is not us, and it is more useful to say so. ### Playwright Open source Best for Teams who want Selenium's cross-browser reach with modern ergonomics — auto-waiting, network interception, and a trace viewer that makes CI failures readable. Trade-off Younger ecosystem than Selenium, and a smaller pool of engineers who already know it. ### Cypress Open source Best for Front-end teams already writing JavaScript who want the best local developer experience in the category. Trade-off JavaScript only, by design and permanently, per the Cypress docs. Not a fit for polyglot QA teams. ### WebdriverIO Open source Best for Teams who want to keep the WebDriver protocol but get the framework layer — runner, reporters, services — that Selenium leaves out. Trade-off Still inherits WebDriver's latency and much of its waiting model. ### Puppeteer Open source Best for Chromium-focused automation, scraping and performance work rather than broad cross-browser test suites. Trade-off Narrow browser coverage compared with everything else here. ### QAEverest Commercial platform Best for Teams whose bottleneck is writing and maintaining the tests rather than executing them — tests generated from the requirement, with traceability back to it. Trade-off A hosted platform, not a library you vendor into your repo. If you want to own the framework layer yourself, pick Playwright. ## Where QAEverest fits ### It starts a step earlier Every tool above assumes you already know which tests to write. QAEverest generates the cases from the user story, flags the acceptance criteria that are too vague to test, and keeps each case traceable to the requirement that justifies it. ### You keep the code Suites export as Playwright, Selenium or Cypress source in your language, with scaffolding, and run without a QAEverest account. Adopting the platform does not mean betting your test estate on us. ### Coverage, not just execution The traceability matrix shows which requirements have no test at all, ranked by business risk — a question no driver or framework answers. ## Questions Should we migrate our whole Selenium suite? Usually not, and certainly not as a first step. A working Selenium suite is paid-for coverage. The common pattern is to leave it running, use QAEverest to generate tests for new stories, and let the traceability matrix show where the old suite has gaps worth filling. Is Playwright a better Selenium alternative than QAEverest? If your problem is the ergonomics of writing browser automation, then yes — Playwright is an excellent answer and we will not pretend otherwise. If your problem is that nobody has time to write and maintain the tests at all, that is a different problem and it is the one we work on. Can QAEverest run our existing Selenium tests? QAEverest generates and runs its own suites and can export to Selenium, but it is not a drop-in runner for an existing hand-written Selenium estate. For that, a grid provider is the better fit. ## Try it against your own backlog Bring ten stories, generate against them, and compare the output with what your Selenium suite covers today. Keep whichever wins. Start free — no credit card See pricing ## Other alternatives guides Playwright alternatives Cypress alternatives Appium alternatives ## Platform comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Something here out of date about Selenium? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Cypress alternatives in 2026 | QAEverest URL: https://qaeverest.ai/cypress-alternatives Summary: Cypress alternatives compared — Playwright, Selenium, WebdriverIO and AI-driven platforms. Built around the trade-offs Cypress documents itself, not around outdated blog-post claims. Skip to main content Alternatives # Cypress alternatives Cypress has the best local developer experience in browser testing, and its team is unusually honest about where the architecture stops. The list below is built from the trade-offs Cypress publishes in its own documentation — which is a more reliable guide than the round-up articles still repeating limitations Cypress fixed years ago. Try QAEverest free Book a demo Cypress limitations taken from its own documentation on 29 July 2026, not from third-party round-ups. ## Why teams look for an alternative - JavaScript is the only language Cypress will ever support — its docs state this as a permanent trade-off, not a roadmap item. That rules it out for QA teams writing Java, Python or C#. - Cypress does not support controlling more than one open browser at a time, which makes genuine multi-user or multi-session scenarios awkward. - Each test is scoped to a single superdomain; crossing origins requires the cy.origin command rather than working transparently. - It is deliberately not a general-purpose automation tool — the docs rule out performance testing, spidering and scripting third-party sites. - No native mobile support, so mobile app coverage needs a second tool regardless. ## What Cypress still does better than anything else For a JavaScript front-end team, Cypress is still the most pleasant way to write and debug a browser test. Tests run in the same loop as the application, the runner's time-travel view makes a failure obvious in seconds rather than minutes, component testing is first-class, and the whole thing works with almost no setup. If your testers are the same people who write the front end, that developer experience is a real productivity asset and none of the alternatives below fully match it. ## The alternatives, and what each is best at Listed with the trade-off that comes with each. For some teams the right answer on this page is not us, and it is more useful to say so. ### Playwright Open source Best for The most common destination for teams outgrowing Cypress: multiple languages, multiple browser contexts at once, genuine cross-origin navigation, and a trace viewer comparable to Cypress's debugging story. Trade-off Local developer experience is very good but not quite Cypress's, and component testing is less mature. ### Selenium / WebdriverIO Open source Best for Teams who need Java, Python, C# or Ruby, standards-based WebDriver, or the widest browser matrix including older targets. Trade-off More assembly required, and waiting strategy becomes your problem again. ### Puppeteer Open source Best for Chromium-only automation and scripted browser tasks. Trade-off Not a test framework, and narrow browser coverage. ### QAEverest Commercial platform Best for Teams who need one tool covering web, API, mobile, performance, security and accessibility, generated from the story rather than hand-written. Trade-off A hosted platform rather than a library in your repo. If you want to keep tests in-repo and hand-written, Playwright is the better move. ## Where QAEverest fits ### One tool past the JavaScript boundary Cypress's language limit is architectural and permanent. QAEverest's suites are generated rather than hand-written, so the language question moves to export time — take the same suite out as Playwright, Selenium or Cypress, in Java, Python, TypeScript or C#. ### Mobile and API in the same platform A Cypress team almost always runs a second tool for mobile and often a third for API. Those run off the same story model here and report into the same coverage view. ### The tests write themselves from the story Cypress makes writing a test pleasant. QAEverest's aim is that most of them do not need writing — generated from the requirement, with the negative and permission paths people skip by hand. ## Questions Is Cypress still a good choice in 2026? For a JavaScript front-end team testing a web application, absolutely. The reasons to leave are structural rather than quality-related: you need another language, native mobile, true multi-browser control, or a wider testing surface than end-to-end web. Playwright or QAEverest as a Cypress replacement? If you want to keep hand-writing tests in-repo, Playwright — it is the natural destination and we would recommend it. Choose QAEverest when the problem is the volume of tests to write and maintain, or when you need mobile, API, security and accessibility under one roof. Where did you get these Cypress limitations? From Cypress's own trade-offs documentation, checked on 29 July 2026, rather than from third-party articles. Several widely repeated Cypress limitations are years out of date, and we would rather link the source than repeat them. ## Try it against your own backlog Bring ten stories, generate against them, and compare the output with what your Cypress suite covers today. Keep whichever wins. Start free — no credit card See pricing ## Other alternatives guides Playwright alternatives Selenium alternatives Appium alternatives ## Platform comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Something here out of date about Cypress? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Appium alternatives in 2026 | QAEverest URL: https://qaeverest.ai/appium-alternatives Summary: Appium alternatives for mobile test automation — Maestro, Espresso, XCUITest, Detox and AI-driven platforms. What each is genuinely best at, and when Appium is still the right call. Skip to main content Alternatives # Appium alternatives Appium is the only mobile automation tool that covers iOS and Android, native and hybrid and mobile web, through one WebDriver-based API. That breadth is exactly why teams evaluate alternatives: the setup surface is large, and a narrower tool aimed at one platform is usually faster and less flaky. Try QAEverest free Book a demo Appium limitations taken from its own documentation on 29 July 2026, not from third-party round-ups. ## Why teams look for an alternative - Setup cost. Xcode, the Android SDK, the right driver versions and a working device or simulator estate all have to line up before the first test runs. - Run speed. The client–server–driver hop makes Appium slower than frameworks that run inside the app process. - Flakiness. Waiting on native UI state across two very different platforms produces intermittent failures that are hard to attribute. - Real-device access still needs a farm, which is a separate cost and a separate integration. ## What Appium still does better than anything else Nothing else covers iOS and Android, native, hybrid and mobile web behind one API, in the language your team already uses, on a standards-based protocol, without vendor lock-in. Every alternative below buys speed or simplicity by narrowing scope — to one platform, one app framework, or one syntax. If you genuinely need one suite across both platforms and several app types, Appium's breadth remains the reason it is still the default. ## The alternatives, and what each is best at Listed with the trade-off that comes with each. For some teams the right answer on this page is not us, and it is more useful to say so. ### Maestro Open source Best for The fastest path from nothing to a running mobile test. Flows are declared in YAML and run on both iOS and Android, with built-in tolerance for the timing issues that cause most mobile flake. Trade-off YAML flows are deliberately simple; complex conditional logic is harder to express than in a general-purpose language. ### Espresso Open source · Android only Best for Google's own Android framework. Runs in-process, which makes it the fastest and least flaky option on Android. Trade-off Android only — you need a second framework for iOS. ### XCUITest Open source · iOS only Best for Apple's own iOS framework, integrated with Xcode and the most stable choice on that platform. Trade-off iOS only, and Swift/Objective-C only. ### Detox Open source · React Native Best for React Native apps. Its grey-box approach synchronises with the app's own state, so it waits for network calls and animations without explicit sleeps. Trade-off Requires instrumenting the app, and is aimed squarely at React Native rather than native apps generally. ### QAEverest Commercial platform Best for Teams who want mobile coverage generated from the same user stories as their web and API tests, executed on connected device farms, reported in one place. Trade-off A hosted platform. For a single-platform native app with an in-house team, Espresso or XCUITest will be faster and cheaper. ## Where QAEverest fits ### Mobile is not a separate project The usual end state is Appium plus a web framework plus an API tool, with three sets of tests nobody can reconcile. QAEverest generates mobile, web and API coverage from the same story and rolls all three into one coverage view. ### The setup surface is ours, not yours Device farm connectivity — BrowserStack, Sauce Labs, LambdaTest or your own grid — is configuration rather than an SDK integration project. ### Requirement-level coverage across platforms The traceability matrix answers which requirements have no mobile test at all, which is the gap that ships bugs on the platform nobody got round to covering. ## Questions Is Appium obsolete? No. It remains the broadest mobile automation tool available and the only standards-based one covering both platforms and several app types. The alternatives here win by being narrower, which is an advantage only if your needs are narrow too. Maestro or QAEverest? If you want an open-source framework your engineers own and your problem is Appium's setup and flake, Maestro is a strong answer and worth trying first. Choose QAEverest when mobile is one part of a testing surface that also includes web, API, performance, security and accessibility, and you want them generated from one set of requirements. Do we still need a device farm? For real-device coverage, yes — and QAEverest connects to BrowserStack, Sauce Labs, LambdaTest or a self-hosted grid rather than replacing them. ## Try it against your own backlog Bring ten stories, generate against them, and compare the output with what your Appium suite covers today. Keep whichever wins. Start free — no credit card See pricing ## Other alternatives guides Playwright alternatives Selenium alternatives Cypress alternatives ## Platform comparisons QAEverest vs Katalon QAEverest vs Functionize QAEverest vs TestMuai QAEverest vs mabl QAEverest vs Testsigma QAEverest vs BrowserStack Something here out of date about Appium? Tell us and we will correct it. ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # About Us — DevQAExpert Solution Pvt Ltd | QAEverest URL: https://qaeverest.ai/aboutus Summary: DevQAExpert Solution Pvt Ltd is the team behind QAEverest and FindMyLocator, delivering AI-powered, time-efficient and budget-friendly software testing since 2015. Skip to main content # DevQAExpert Solution Pvt Ltd QAEverest and FindMyLocator represent our commitment to staying at the forefront of testing technology. Since 2015, DevQAExpert has been a leader in innovative testing solutions. We offer time-efficient and budget-friendly services that leverage the latest technologies for unmatched test coverage. DevQAExpert is the innovative force behind QAEverest and FindMyLocator. We are dedicated to empowering businesses with cutting-edge testing tools that streamline processes and ensure exceptional quality. ## OUR MISSION & VALUES At DevQAExpert, we exist to make software quality intelligent, accessible, and within reach for every team — regardless of size, budget, or technical depth. ### Innovation AI-first, always pushing the boundary of what software testing can achieve. ### Quality First We build tools that teams can trust with their most critical software. ### Speed to Ship From user story to execution — every second in the QA cycle counts. ### Customer Success Our measure of success is the productivity and confidence of your team. ## OUR LEADERSHIP The people behind QAEverest — a leadership team combining deep QA expertise, engineering excellence and AI innovation. ### Pooja Kabra CEO & Founder Sets the vision and strategic direction of QAEverest. Pooja drives product strategy, customer success and company growth — ensuring every capability we ship moves teams closer to intelligent, effortless software quality. ### Rupesh Kabra MD & Founder Leads business operations, partnerships and long-term expansion. Rupesh oversees governance and financial strategy, building the foundation that lets QAEverest scale sustainably while staying budget-friendly for every customer. ### R. P. Adusumilli Member of Board of Advisory A seasoned technology and business leader with deep expertise in quality engineering and digital transformation. Through senior leadership roles at AppLabs, TestingXperts, Enhops, and Quality Matrix, he has driven business growth, built high-performing teams, and delivered strategic initiatives for global clients. ### Suman Acharya Member of Board of Advisory Brings over 18 years of quality engineering and test management experience to the advisory board. A Test Manager at GCS in Reading, UK, Suman specialises in test strategy, defect leakage analysis and translating complex requirements into airtight coverage — grounding our roadmap in what delivery teams actually need. ### Arti Tongia Development Head Owns engineering delivery across the QAEverest platform. Arti leads the development team, platform architecture and release quality — turning ambitious product ideas into reliable, production-grade features. ### Pranjal Dubey AI Head Drives the AI engine at the heart of QAEverest. Pranjal leads our LLM-powered agents, model integrations and research — from AI test generation to self-healing automation and intelligent root-cause analysis. ### Abhilasha Prasad Technical Head & Manager Guides technical direction and cross-team execution. Abhilasha manages delivery across automation, integrations and infrastructure — ensuring the platform teams rely on every day stays fast, stable and secure. ## OUR PRODUCTS Two products, one goal — helping teams ship better software faster, from the first user story to the final automated run. AI-Powered Testing Platform ### QAEverest From One Story, Unleash a Legion of Tests. QAEverest is your end-to-end AI-powered testing platform — covering everything from user story creation and intelligent test case generation to full-stack automation across web, API, mobile and desktop applications. - AI Test Generation Across All Types: Instantly generate Functional, System, API, Security and Performance test cases from user stories, documents or Jira/ClickUp tickets — in Traditional or Gherkin (BDD) format. - Full-Stack No-Code Automation: Execute automated tests across Web UI, REST/SOAP/GraphQL APIs, Android, iOS and Desktop applications — with CI/CD integration, video recording and AI-powered failure analysis. - Story Creation to Execution in One Platform: Transform vague requirements into precise, testable user stories with AI; then generate, automate and execute tests — all without switching tools. Browser Extension ### FindMyLocator Every Web Element, Located in a Single Click. Quickly find any web element with our powerful and user-friendly browser extension. Perfect for developers and testers who need reliable locators without digging through the DOM. - Effortless Element Identification: Locate unique elements within the Document Object Model (DOM) structure with just a single click. No more searching! - Advanced Selector Generation: FindMyLocator automatically generates unique selectors for the identified elements, saving you valuable time and effort. - Axes Support: Utilize advanced axes methods like siblings, following-sibling, parents, childNodes, ancestors and descendants for precise element targeting. ## OUR ACHIEVEMENTS Awarded "Most Trusted Software Testing Company in Central India" by the National Quality Awards 2023 Recognized as 20 Most Promising Startups to Watch-2023 by Business Connect India AI-based Software Testing Device ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Benefits of AI-Powered Testing: ROI & Coverage | QAEverest URL: https://qaeverest.ai/yourbenefits Summary: Experience the power of AI in testing with QAEverest. Generate test cases effortlessly, improve test coverage, and accelerate software development. Skip to main content # Save Testing Costs by Up to 98%* with QAEverest Turbocharge Your QA — 90x* Faster Test Automation, Zero Complexity! *Based on internal analysis comparing average manual QA costs and timelines. Trusted by QA teams across startups, enterprises, and agencies — from the first test case to the CI release gate. 98% Cost Savings 10X Faster Test Creation 90% Better Test Coverage 88%+ Accuracy Rate ## Built for Every Team in Your Organisation Whether you’re a startup founder or an enterprise QA lead, QAEverest has a tailored solution that fits your workflow. ### Startups Get enterprise-grade testing capabilities without overextending your runway. AI-powered tools help you scale fast, ensuring quality from day one without hiring a full QA team. View Plans → ### Quality Assurance Teams Automate repetitive testing tasks, accelerate release cycles, and ensure comprehensive coverage across APIs, UI, Mobile, Performance, and Security — all from one platform. Explore Features → ### Business Analysts / Stakeholders Transform vague requirements into precise, testable user stories. AI detects gaps, ambiguities, and missing scenarios before development begins — reducing rework and misalignment. See Story Creation → ### Developers Validate builds with integrated testing workflows, catch bugs early, and reduce rework through schema validation, API automation, and AI-generated test data management. Explore API Automation → ### Product Managers Ship with confidence. Maintain RTM traceability, monitor coverage and quality insights through dashboards, and make data-driven release decisions — not gut-driven ones. See Product Overview → ### Enterprises, Agencies & Partners Streamline testing at scale with native Jira and ClickUp integration, enterprise-grade security, and a white-label QA solution designed for agencies delivering to multiple clients. Contact Sales → ## What QAEverest Can Do Constant innovation keeps your QA ahead of the curve. Every capability below is available today — end-to-end, on one platform. ### Time-Travel Replay Scrub frame-by-frame through any UI test run. AI root-cause analysis surfaces the exact failed step, screenshot, and network call. ### Test Health & ROI Dashboard Quantify automation value: healed locators, quarantined flaky tests, failure clusters, and engineer-hours saved — metrics your team and stakeholders both need. ### Autonomous Exploratory Testing Point at a URL and walk away. AI logs in, crawls the app, generates Gherkin flows, executes them live, and triages failures into Jira bug cards in one click. ### Visual Regression Detection Pixel-level screenshot comparison with baseline management. Catch layout regressions across builds before users ever see them. ### Multi-Platform TCM Export Push test cases to TestRail, Zephyr Scale, Qase, QTest, PractiTest, or Azure Test Plans in one click — no copy-paste, no re-entry, no sync drift. ### Quality Command Center One ship-confidence score — functional pass-rates, RTM coverage, flakiness, and CI release gates — so release decisions are data-driven, not gut-driven. ### AI Copilot Assistant Ask "Where are my coverage gaps?" or "Run my last failed suite" in plain English. Copilot streams live tool actions and replies with structured answers. ### Figma Design Import Import live Figma frames directly into test generation. Design specs become test coverage automatically — no manual transcription, no drift. ## Put a number on it Enter your testers, QA salary, regression cycles, suite size, and release frequency — and see the hours, cost, and release time QAEverest gives back in year one. Open the ROI Calculator → ## How QAEverest Compares Detailed breakdowns across cost, speed, accuracy, and capability — so you can see exactly what changes when your team adopts QAEverest. 01 QAEverest vs Traditional Testing Process ## Unlock the Power of AI Testing: QAEverest vs Traditional Process See how AI-driven testing replaces costly, slow manual workflows — from test case generation to delivery, execution, and export. Comparison of QAEverest AI testing tool versus traditional manual testing process Feature QAEverest Traditional Process Test Case Generation AI generates hundreds of test cases per minute from user stories, images, or documents. Manual creation — a skilled tester produces roughly 20 test cases per day. Test Case Types Functional, System, API, Security & Performance — all generated in a single click. Typically one type per session; Security and Performance are rarely prioritised. BDD / Gherkin Support Full Given–When–Then scenario generation alongside traditional TDD format. Requires a separate BDD framework and significant manual scripting. Download & Export Formats Always available in 12+ formats: XLSX, CSV, PDF, Word, JSON, BDD — plus direct push to Zephyr, TestRail, QTest, QBase, Azure, and PractiTest. Stored in spreadsheets or documents; format conversion is manual and error-prone. Story Refinement AI clarifies vague user stories, detects missing scenarios, and refines requirements before a single test case is written. Requires lengthy BA workshops; gaps typically surface only during test execution. API Automation Built-in engine for REST, SOAP, and GraphQL; imports Swagger/Postman/WSDL; schema validation and data injection from Excel, CSV, or databases. Requires a separate framework (Postman, RestAssured); all setup and maintenance is manual. UI Automation Built-in cross-browser automation with self-healing locators, video recording, screenshot capture, and consolidated dashboards. Requires a separate framework (Selenium, Playwright); setup, maintenance, and licences add significant cost. Mobile & Desktop Automation Cloud-based execution on real devices, emulators, and simulators with full CI/CD pipeline integration. Separate tooling required (Appium, Espresso); no unified desktop automation support. Visual Regression Detection Pixel-level AI screenshot comparison with baseline management — catches layout regressions across builds automatically. Manual visual review only; no systematic baseline tracking or automated comparison. Time-Travel Replay Scrub frame-by-frame through any test run; AI pinpoints the exact failed step with screenshot and network call context. No replay — the entire test must be re-run from scratch to investigate a failure. Multi-Platform TCM Export One-click push to TestRail, Zephyr Scale, Qase, QTest, PractiTest, or Azure Test Plans — zero copy-paste, zero sync drift. Manual copy-paste into each TCM platform; high risk of version inconsistency. Integrations Native Jira, ClickUp, and Notion project integrations; test case export to Zephyr Scale, TestRail, QTest, QBase, Azure Test Plans, and PractiTest; API source import from Swagger, Postman, and WSDL — all without custom code. Custom API development required for each integration, with ongoing maintenance overhead. Scalability Scales to thousands of test cases within the same subscription — zero extra headcount cost. Each additional tester adds $30,000–$80,000 per year in salary, benefits, and overhead. Consistency & Accuracy 88%+ AI accuracy; identical output on every run with no human fatigue or oversight errors. Accuracy depends on individual tester skill; inconsistency rises sharply under deadline pressure. Long-term Investment Subscription-based; early deployments show up to 98% cost reduction versus equivalent manual headcount. Recurring salaries, training, tooling, and benefits — total cost compounds significantly at scale. 02 Time & Cost — Measurable Savings Breakdown ## Faster Testing, Higher Quality, and Measurable ROI With QAEverest, unlock the power of automation to generate comprehensive test cases in seconds — saving time, reducing costs, and ensuring maximum coverage far beyond what manual testing can achieve. QAEverest savings comparison versus manual testing Metric Without QAEverest With QAEverest Your Saving Test Cases Created A tester produces ~20 test cases per day. Creating 2,400 test cases takes 120 working days (~6 months). QAEverest generates 2,400+ test cases in approximately one working day. 119 working days Cost for 2,400 Test Cases At $5/hr × 8 hrs × 120 days = ~$4,800 in labour alone — before overhead and tooling. QAEverest generates the same volume for approximately $45. $4,755 saved (99%) Test Case Accuracy Prone to human error, missed edge cases, and inconsistency — especially under deadline pressure. AI-generated test cases achieve 88%+ accuracy with full edge-case and negative-flow coverage. Up to 88% fewer errors Test Coverage Coverage depends on tester experience; complex scenarios and corner cases are frequently missed. Automated coverage analysis captures up to 90% of scenarios including edge cases. Up to 90% coverage Scalability Cost Every volume increase requires additional testers, each adding $30,000–$80,000/year in cost. Scale to any volume within the existing subscription — zero incremental headcount. Zero extra hiring Time to First Automation Setting up automation frameworks and writing the first scripts typically takes 2–4 weeks of engineering time. AI generates automation-ready test scripts directly from user stories — live within hours. 10X faster start 03 QAEverest vs Other AI Testing Tools ## QAEverest: Outperforming Other AI Testing Tools When selecting an AI testing tool, evaluate depth — not just marketing claims. Here is a comprehensive, feature-by-feature breakdown of where QAEverest leads. QAEverest AI testing tool compared to other AI testing tools Feature QAEverest Other AI Tools AI Test Case Generation Domain-aware; Functional, System, API, Security & Performance types; TDD and BDD/Gherkin output. Generic output; usually functional only; rarely supports BDD. Story Refinement AI-powered requirement review, missing-scenario detection, and image/document upload support. Not available in most tools. API Automation (Built-in) REST, SOAP, GraphQL; Swagger/Postman/WSDL import; schema validation; data injection. External framework required (Postman, RestAssured); no built-in engine. UI Automation (Built-in) Self-healing locators; video recording; cross-browser; CI/CD integration; detailed dashboards. Not available; requires separate Selenium or Playwright setup. Mobile & Desktop Automation Cloud execution on real devices, emulators, and simulators; full CI/CD pipeline support. Separate tooling required (Appium, Espresso); no desktop automation. Time-Travel Replay Frame-by-frame scrubbing with AI root-cause analysis per step, screenshot, and network call. Not available. Visual Regression Detection Pixel-level screenshot comparison with automated baseline management. Not available. Multi-Platform TCM Export One-click push to TestRail, Zephyr Scale, Qase, QTest, PractiTest, and Azure Test Plans. 1–2 export formats at most; no direct TCM platform push. Download Formats XLSX, CSV, PDF, Word, JSON, BDD/Gherkin — plus 6 direct TCM platform exports. Typically Excel or PDF only. Integrations Jira, ClickUp, Notion — native, no custom code required. Very limited or entirely unavailable. Collaboration Shared workspace; multi-user editing; reviewer mode; full version history and change logs. Single-user only; no collaboration or version tracking. Test Case Insights Gap analysis, quality scoring, missing-scenario detection, and AI-powered recommendations. Raw output only; no analytics, insights, or diagnostics. Accuracy & Diagnostics 88%+ AI accuracy; visual and domain-based interpretation; AI-powered failure root-cause. Variable accuracy; no built-in diagnostics. User-Friendliness Low-code; intuitive UI designed for manual testers and non-technical users. Steep learning curve; often requires developer setup and scripting knowledge. Security Encrypted storage; secure dashboard; enterprise-grade data protection. Security posture unclear or undocumented. Quality Command Center Ship-confidence score across functional pass-rates, RTM coverage, flakiness, and CI gates — in one unified view. Not available. AI Copilot Assistant Natural-language interface: "Run my last failed suite" or "Show coverage gaps" — streamed live with structured answers. Not available. Figma Design Import Import live Figma frames into test generation — design specs become test coverage automatically. Not available. 04 QAEverest vs General-Purpose AI (ChatGPT & LLMs) ## QAEverest vs General-Purpose AI: Purpose-Built Beats Prompting General AI assistants like ChatGPT, Claude, Gemini, and Copilot can draft test ideas — but they can’t run, track, integrate, or govern your testing. Here is what changes when QA is the product, not a prompt. QAEverest AI testing platform compared to general-purpose AI assistants such as ChatGPT Feature QAEverest General-Purpose AI Purpose Purpose-built QA platform tuned for software testing across functional, API, UI, mobile, performance & security. General-purpose chatbot with no testing domain model, workflow, or guardrails. Test Case Generation Hundreds of structured, de-duplicated test cases per run with consistent IDs, format, and edge/negative coverage. Ad-hoc cases inside a chat; volume, structure, and quality vary with every prompt. Project Context & Memory Persists projects, stories, requirements, and run history across sessions. Limited context window; loses project context between chats. Test Execution & Automation Actually runs API, UI, mobile & desktop tests and reports results — not just suggestions. Writes text or code snippets only; cannot execute tests or report outcomes. Requirement Traceability (RTM) Built-in requirement-to-test traceability and coverage gap analysis. No traceability; cannot reliably map tests back to requirements. Export & TCM Integration One-click export to XLSX, CSV, PDF, Word, JSON, BDD — plus direct push to TestRail, Zephyr, QTest, Azure, and more. Copy-paste from chat; manual reformatting; no TCM platform integration. Tool Integrations Native Jira, ClickUp, and Notion; Swagger/Postman/WSDL import — no custom code. No native integrations; everything moved in and out by hand. Accuracy & Consistency 88%+ accuracy with identical structured output on every run. Output drifts between runs; prone to hallucinated, duplicate, or incomplete cases. Collaboration & Versioning Shared workspace, multi-user editing, reviewer mode, and full version history. Single-user chat; no shared workspace or version control. Dashboards & ROI Insights Coverage analytics, quality scoring, Test Health, and engineer-hours-saved ROI dashboards. No analytics, dashboards, or reporting of any kind. Data Security & Governance Encrypted storage, enterprise-grade controls, with SSO and audit logging on enterprise plans. Prompts may be retained or used for model training; data governance is often unclear. Total Cost & Effort Flat subscription covering generation through execution and export — predictable at any scale. Token costs plus heavy manual effort to assemble, format, and maintain a usable suite. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest Partnership & Enterprise Solutions URL: https://qaeverest.ai/partners Summary: Explore QAEverest partnership models — white labelling, enterprise client and a free partner referral programme. Grow your business with AI-powered QA. Skip to main content Partner programme # Grow your business with AI-powered QA White-label, resell or refer QAEverest.ai. Pick the partnership model that fits — from fully branded platforms to commission on every referral. Become a partner Talk to sales Trusted by agencies, resellers and consultancies to deliver QA under their own brand. 3 partnership models Free to join as referral partner Recurring commission on subscriptions SLA enterprise-grade support Partnership models ## Choose how you want to grow From fully white-labeled platforms to referral-based commission — pick the model that best suits your business. ### White labeling Own-branded QA testing solution with dedicated infrastructure Best for: Agencies & resellers - Dedicated branding: Your logo and brand identity - Dedicated server: Enhanced security and performance - Custom URL: Your own domain (e.g. testing.yourdomain.com) - Separated database: Isolated data management for compliance - Custom feature modifications: Tailored to your requirements - Priority support: Premium technical assistance - Role-based access control: Custom permissions hierarchy - Data ownership & compliance: Full control over your data One-time setup Custom quote Ongoing maintenance Based on active users / month Get white label quote Most popular ### Enterprise client Advanced features and scalability for large organizations Best for: Large organizations & teams - Organizational-level access: Multi-user and team support - Shared cloud infrastructure: High-performance scalable servers - Standard URL access: Via QAEverest.ai domain - Multi-tenancy database: Secure data management with logical separation - Custom integrations: Work with your existing tools - API integration: Seamless workflow integration - Enterprise-grade support: SLA-based assistance - Regular updates: Continuous feature improvements Pricing model Custom based Get enterprise quote ### Free partner model Earn commission by referring clients to QAEverest.ai Best for: Consultants & affiliates - Commission: Competitive commission on all referred subscriptions - Applicable to: Monthly, quarterly, annual & enterprise subscriptions - Referral tracking: Dedicated portal to monitor earnings - Marketing support: Materials to help promote QAEverest.ai Cost to join Free Become a partner Getting started ## How the partner programme works Four simple steps from application to your first payout. 1 ### Apply Pick a model and tell us about your business. 2 ### Get approved Onboarding, agreement and portal access. 3 ### Refer or launch Share referrals or go live with your brand. 4 ### Earn & grow Track earnings and scale with support. Why QAEverest ## Built for partners to win Everything you need to sell, deliver and grow recurring QA revenue. ### Recurring revenue Commission that keeps paying on renewals. ### Your brand, our tech Full white-label control of look and domain. ### Dedicated support SLA-based help and onboarding for partners. ### Secure & compliant Isolated data, RBAC and ownership controls. ### Co-marketing & enablement Marketing materials and joint go-to-market support. ### Fast onboarding Get approved and live quickly with guided setup. ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest for Enterprise — Secure, Scalable AI-Powered QA URL: https://qaeverest.ai/partners/enterprise Summary: Enterprise QA built on trust: SAML SSO, role-based access, audit logging, account security and on-premises or VPC deployment for AI-powered testing at scale. Skip to main content Partners / Enterprise client For large organizations # Enterprise QA, built on a foundation of trust Scale AI-powered testing across every team with the security, control and compliance large organizations require — in our cloud, or fully inside your own network. Get enterprise quote Book a demo SAML 2.0 SSO Role-based access Audit logging SOC 2-aligned On-prem / VPC SLA enterprise support SSO Okta · Azure AD · Google 1-yr audit retention On-prem run in your VPC Built for scale ## Everything large teams need Multi-team access, scalable infrastructure and the integrations your organization already runs on. ### Org-level access Multi-user, multi-team structure with central administration. ### Scalable cloud High-performance shared infrastructure that grows with you. ### Data isolation Per-organisation data separation, enforced on every query. ### Custom integrations Connect Jira, Slack, ClickUp and the tools you already use. ### API access Drive QAEverest from your own pipelines and workflows. ### SLA-based support Dedicated enterprise assistance with guaranteed response. Security & governance ## The Enterprise Trust Layer The controls procurement and security teams ask for before they sign — included, not bolted on. ### Single sign-on Log in through your own identity provider. - SAML 2.0 IdP - JIT provisioning - Per-org connection ### Custom roles & permissions Build roles from 24 granular permissions. - 24-permission catalog - 4 role presets included - Enforced server-side ### Audit logs Who, what, when, from where — recorded. - Filter by actor/date - CSV export - 1-year retention ### Account security Brute-force lockout and configurable session controls. - Configurable failed-login lockout - Session timeout policy - Lockout events audited - Admin-set thresholds ### On-prem / VPC Deploy the platform inside your own network. - Fully containerised deployment - Inference via your own AWS account - Bring your own DB & TLS - Full Trust Layer included Deployment ## Your cloud or ours Start fast on our managed cloud, or deploy the entire stack inside your own infrastructure. ### Managed cloud Multi-tenant SaaS - Live in minutes — nothing to host - Scalable shared infrastructure - Logical multi-tenant isolation - Automatic updates ### On-premises / VPC In your network · guided rollout - Fully containerised deployment - Test data stays in your database; AI inference runs through your own AWS Bedrock account - Documented outbound: Bedrock endpoint and your SMTP only - Bring your own database & TLS - Full Trust Layer included ## Roll out QAEverest across your organization Custom pricing scaled to your team. Talk to us about cloud or on-prem, SSO setup, and a security review. Get enterprise quote Request Trust Layer overview ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # White Label QA Platform — Resell Under Your Brand | QAEverest URL: https://qaeverest.ai/partners/white-labelling Summary: A fully white-labeled, AI-powered QA platform: your logo, your domain, dedicated server and isolated database. We maintain the engine; you own the client. Skip to main content Partners / White labelling For agencies & resellers # Sell QA under your own brand A fully white-labeled, AI-powered QA platform — your logo, your domain, your dedicated infrastructure. We build and maintain the engine; you own the client relationship. Get white label quote Talk to sales Your logo Custom domain Dedicated server Separate database RBAC Your brand logo · colors · domain Dedicated server & database Custom feature modifications Priority partner support Make it yours ## Your clients never see QAEverest Every touchpoint carries your identity — from the login domain to the logo in the corner. - Your logo & colors across the entire app - Your domain — e.g. testing.yourbrand.com - Dedicated server for performance & security - Isolated database for compliance & data ownership testing.yourbrand.com Y YourBrand QA Dashboard · Tests · Reports Run test suite Your logo Your domain Your colors What's included ## A complete platform to resell Everything you need to deliver AI-powered QA as your own product. ### Dedicated branding Your logo and brand identity throughout. ### Custom URL Your own domain for the platform. ### Dedicated server Enhanced security and performance. ### Separated database Isolated data for compliance. ### Custom features Modifications tailored to your needs. ### Role-based access Custom permissions hierarchy. ### Priority support Premium technical assistance. ### Data ownership Full control over your data. Getting started ## From quote to launch We handle provisioning and maintenance so you can focus on selling. 1 ### Scope & quote Tell us your needs; get a custom quote. 2 ### Brand & provision Logo, domain, server and database set up. 3 ### Launch Go live on your own branded platform. 4 ### Sell & support Own the client relationship; we maintain. Pricing ## Simple, scalable pricing A one-time setup, then maintenance that scales with your active users. One-time setup Custom quote Branding, domain, dedicated server & database provisioning. Ongoing maintenance Per active user / mo Hosting, updates and priority partner support. ## Launch your own QA platform Get a tailored white-label quote and go to market under your brand. Get white label quote Talk to sales ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest Channel Partner Programme — Recurring Referral Commission URL: https://qaeverest.ai/partners/agent Summary: Join free, refer clients and earn recurring commission on every QAEverest subscription — monthly, quarterly, annual and enterprise. Track earnings in your portal. Skip to main content Partners / Channel Partner For consultants & affiliates # Refer clients. Earn recurring commission. Join free, share QAEverest with your network, and earn competitive commission on every subscription you refer — for as long as your clients stay. Become a channel partner How it works Free to join Recurring payouts Referral portal Marketing kit Free cost to join Recurring commission All plans monthly → enterprise Portal track earnings Your earnings ## Commission that keeps paying Track every referral and watch recurring commission grow with each renewal — all from a dedicated portal. - Competitive commission on all referred subscriptions - Applies to every plan — monthly, quarterly, annual & enterprise - Paid on renewals , not just the first sale - Real-time tracking in your referral portal Referral portal Illustrative 12 Referrals 9 Active ↑ Recurring Jan Monthly commission Jun Why become a channel partner ## Built for consultants & affiliates Everything you need to refer with confidence and get paid reliably. ### Competitive commission Earn on every referred subscription. ### Applies to all plans Monthly, quarterly, annual & enterprise. ### Recurring on renewals Commission that keeps paying over time. ### Referral tracking Dedicated portal to monitor earnings. ### Marketing support Materials to help you promote QAEverest. ### Free & fast onboarding No cost to join; get started quickly. How it works ## Four steps to your first payout From sign-up to recurring commission — no cost, no catch. 1 ### Apply free Sign up as a channel partner in minutes. 2 ### Get your link Receive a referral link & portal access. 3 ### Refer clients Share QAEverest with your network. 4 ### Earn & grow Get paid on every subscription & renewal. ## Start earning with QAEverest It's free to join. Refer your first client today and earn recurring commission. Become a channel partner Talk to us ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # /contact-us URL: https://qaeverest.ai/contact-us Summary: Get in touch with QAEverest for a free consultation and discover how our AI-powered testing platform can transform your software testing processes. Reach out today! Skip to main content # Contact QAEverest — Book a Demo or Talk to Sales Let's get this conversation started. Tell us a bit about yourself, and we'll get in touch as soon as we can. Contact Us Form First Name * Last Name Email * Contact Number Country * State Organization Name Role Select Developer QA Engineer Admin Project Manager DevOps Business Analyst (BA) Product Owner Other Message * Submit India Office: 353 Clerk Colony, Near MPSEDC IT Park, Electronic Complex Pardeshipura, Indore 452011, (MP), India USA Office: San Ramon, 123 Innovation Drive, Suite 400, CA 94583, USA Email: support@qaeverest.com ## Explore Now! Book a Demo Become a Partner ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest FAQ — AI Test Case Generation Questions Answered URL: https://qaeverest.ai/faq Summary: Answers on QAEverest Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # QAEverest — Frequently Asked Questions ## What is the purpose of QAEverest? The purpose of QAEverest is to automate and streamline the creation of test cases for software applications. Here's a breakdown of its key functionalities: - Web Application: Accessible through a web browser for user convenience. - User Registration: Ensures authorized access and potentially user management features. - User Story/Scenario Input: Users provide a description of the application's functionality using user stories or scenarios. - Automated Test Case Generation: QAEverest analyzes the user input and generates multiple test cases covering various aspects. - Test Case Types: It supports a comprehensive range of testing types: - Functional Testing: Focuses on core functionalities and user interactions within the application. - System Testing: Validates the entire system as a whole, ensuring seamless integration of all components. - Performance Testing: Evaluates the system's responsiveness, stability, and scalability under varying workloads. - Security Testing: Identifies vulnerabilities and weaknesses in the system to protect against unauthorized access and data breaches. - API Testing: Tests Application Programming Interfaces used for data exchange between different parts of the application or with external systems. - Subcategories: Each main test case type likely has subcategories for more detailed testing. These subcategories ensure a thorough examination of various aspects within each testing type. ## How can I sign up with QAEverest? The registration process for QAEverest is as follows: - Visit the QAEverest Website: Navigate to the QAEverest website using your web browser http://qaeverest.ai/. - Locate the Sign-Up: Look for a button or link labeled "Sign Up," "Register," or something similar. - Fill Out the Registration Form: Complete filling your details in the registration form. Once you've filled out the form with your details, click the "Register" button. - Verify Your Email Address: QAEverest will likely send a verification email to the address you provided. This email contains a link you need to click to confirm your email address. - Login to QAEverest: After successful email verification, you can now log in to QAEverest using your email address and the password you created during registration. ## How can I sign in to QAEverest? To sign in to your QAEverest account, follow these steps: - Enter Your Credentials: In the designated fields, enter the email address and password you used during registration. - Click the Sign in Button: Once you've entered your credentials, click the "Sign In" button to initiate the login process. ## How can I generate test cases? - Click on "Generate Test Cases." - Provide Details: - Title: Enter a clear and concise title for your test suite. This should reflect the functionality or area being tested. - Domain: Select the relevant domain or area of the application you're testing (e.g., Login, Shopping Cart, Search). - Module: If applicable, choose the specific module within the domain (e.g., User Management, Product Details). - Story or Scenario: You might have an option to specify a user story or scenario you're basing the tests on. If available, use this to provide additional context. - Type of Testing: Select the primary type of testing you want to focus on. The note mentions positive testing is included by default, so you might have options for additional types like: - Negative testing (testing with invalid inputs) - Boundary value analysis (testing with extreme values) - Performance testing (testing speed and stability under load) - Usability testing (testing ease of use) - Once you've filled in all the details, click the "Generate TestCase" button. ## How can I save my profile? - Go to personal details page under My Profile section - Update your profile and click on “Save Profile” button ## Is there any free plan? QAEverest offers a free plan with the following deliverables: - Functional Testing - Performance Testing - Security Testing - System Testing - API Testing - 20 Credits - Validity of 7 Days ## What are credits, and how can I use them? Credits are a unit used to represent the cost of generating test cases. Here's a breakdown of how they work: - Value of a Credit: One credit is equivalent to one user story. A user story describes a specific functionality or feature you want to test in your application. - Using Credits: When you provide a user story in QAEverest, it consumes one credit from your plan. In return, QAEverest generates multiple test cases based on your user story. To use credits there are different categories of plan: - Free Plan: Includes 20 credits for initial use. Each credit lets you generate test cases from one user story. - Paid Plans: These plans offer a set number of credits (e.g., 120 credits, 500 credits) that you can use throughout the plan's validity period (monthly, quarterly, annually). With more credits, you can create more user stories and generate more test cases. ## My credits ran out — what should I do? These are the Possible Reasons: - Free Plan Exhaustion: You used all the credits offered in the free plan and generated the maximum allowed test cases. - Paid Plan Expiration: Your paid plan (monthly, quarterly, or annual) has expired, and you've used all the credits allotted within its validity period. - Active Plan Credit Depletion: You've used all the credits within your active paid plan. Expired Plan: - Renew Your Plan: Renew your existing plan (monthly, quarterly, or annually) to get a fresh pool of credits. - Upgrade to a Different Plan: If your previous plan didn't provide enough credits, consider upgrading to a plan with a higher credit allotment. Solutions: 1. Check Your Plan Details: - Review your current plan to understand: - The number of credits it included. - Its validity period (if applicable). 2. Choose a Solution Based on Your Plan: - Free Plan: - Limited Options: Free plans typically have limited functionalities. Your options might be: - Upgrade to a Paid Plan: Choose a paid plan with a credit amount that aligns with your testing needs - Paid Plan: - Expired Plan: - Renew Your Plan: Renew your existing plan (monthly, quarterly, or annually) to get a fresh pool of credits. - Upgrade to a Different Plan: If your previous plan didn't provide enough credits, consider upgrading to a plan with a higher credit allotment. ## Is there any way I can view my generated test cases? Yes, you can definitely view your generated test cases on QAEverest. Here's how: - Log in to your QAEverest account and navigate to the "MyTestCases" section. - Within "MyTestCases," go to "Testcase History". All the test cases you've generated previously are displayed here. - Here you can view the details of each test case. Additionally, QAEverest offers a download option, allowing you to save a copy of the test cases for your records. ## Is my data secure? QAEverest maintains the security of users’ data and test cases through trusted third-party services as follows: AI Provider We use an AI provider to generate test cases based on user stories. The AI provider processes user stories and generates stories to provide the requested service. Payment Gateway We use a third-party payment gateway to process payments. Payment information is handled in accordance with the payment gateway's privacy policy and security measures. Cloud Storage We store data on third-party cloud storage services, which includes profile information, user stories, and generated stories. ## What are the terms and conditions to use QAEverest? For Terms & condition refer Terms & condition ## Can a company/organization use this product? Yes, for now an organization can use our app by registering as an individual. ## What payment provider are we using? QAEverest uses Razorpay, a well-established payment gateway provider in India known for its secure and reliable transactions. Here's what this information implies: - Security: Razorpay adheres to strict security standards like PCI compliance, ensuring your payment information is protected during transactions. - Convenience: Razorpay offers a variety of payment methods, including credit cards, debit cards, net banking, UPI wallets, and more, making it convenient for users to pay. - Global Reach: While potentially focused on India, Razorpay does facilitate international payments in some cases. ## I need to pay in INR, can I pay? Yes, you can definitely pay in INR! Here's a breakdown for clarity: - QAEverest Payment Gateway: Razorpay - Supported Currencies: USD/INR ## I am non-Indian. Can I use the system? QAEverest being designed for everyone and supporting the English language makes it a good option for you, even though you're non-Indian. Here's a breakdown of the benefits for you: - Language Support: Since QAEverest supports English, you can comfortably use the interface, provide user stories, and understand the generated test cases. - Global Applicability: QAEverest's functionalities are likely not restricted to a specific region or industry. ## Generated test cases are not good, what should I do? - Write clear, detailed user stories - Positive and Negative Scenarios - Use sample test case provided - Break down complex features - Contact support if needed support@qaeverest.com ## Please guide me on how I can generate the test cases effectively. Crafting Powerful User Stories: - Write user stories that are easy to understand, with concise language and a clear flow of actions. - Include specific details like login credentials, expected results, and data points relevant to the functionality being tested. - Include both positive and negative test cases within your user stories. Consider scenarios where login might fail, searches might return no results, or unexpected errors might occur. - Example (positive): "The user logs in with a valid username and password." - Example (negative): "The system displays an error message when the user enters an invalid username." - If you're dealing with intricate functionalities, break them down into smaller, more manageable user stories. This enhances clarity and helps the AI generate more focused test cases. - Refer to Sample Test Case provided in help icon. ## I have a payment issue, to whom do I need to contact? QAEverest uses Razorpay for payment processing and payment issues are typically handled by the payment gateway, here's how you can get help with your payment issue: Contact QAEverest Support: Please report to QA Everest's support team at support@qaeverest.com . ## The system generates an error, what should I do? Recommended Steps: - Try Again Later: Temporary glitches or server overload can sometimes cause errors. Waiting a while and retrying your action might be enough to resolve the issue. - Check Credit Balance: If the error is related to insufficient credits, you can likely view your credit balance within your QAEverest account. - Plan Expiry: Ensure your plan hasn't expired. Expired plans might restrict certain functionalities or generate errors when attempting to use them. Renew your plan if necessary. - Contact Support: If the error persists, contacting QAEverest's support team at support@qaeverest.com ## How can I report a bug? To report a bug, go to https://qaeverest.ai/HomeReportABug and fill in the details. Submit the bug by clicking the Submit button. ## How can I view my payment history? Steps to View Payment History To view your payment history, open a web browser and navigate to: https://qaeverest.ai/billingHistory - Login: You'll likely need to log in to your QAEverest account to access your payment history - Billing Section: Once logged in, look for a section "Payment Details" ## How can I edit generated test cases? Editing Generated Test Cases in QAEverest: Download for Editing: - Generate Test Cases: Create your test cases using QA Everest's AI generation. - Click on Export to download the test cases. - The testcases are downloaded and this allows you to edit the test cases using familiar spreadsheet software. ## Why did my account get archived/locked? Your account on QAEverest might be archived for two reasons: - Plan Expiration: Your subscription plan might have expired, and you haven't renewed it yet. - Inactivity: If you haven't used your account for a long time, QAEverest might archive it to manage their resources. Although archived, you can still access your generated test cases in this mode. If you want to regain full functionality of your account, here are your options: - Renew your subscription plan (if applicable). - Contact QAEverest support for help reactivating your account by emailing support@qaeverest.com ## What types of documents can I upload for data extraction? QAEverest.ai can extract data from various document formats, including: - Documents (DOCX, PDF) - Images (JPG, PNG) - Spreadsheets (XLSX, CSV) - Presentation Files (PPTX) ## Which project management tools does QAEverest.ai integrate with? Currently, QAEverest.ai seamlessly connects with Jira and ClickUp. ## What is the minimum and maximum resolution for the images accepted? - Minimum resolution: 200x200 pixels - Maximum resolution: 4000x4000 pixels ## What is the maximum file size allowed for uploads to QAEverest.ai? The maximum file size for uploads to QAEverest.ai is 20MB per file. ## How many files can I upload at once? You can upload a maximum of 5 files at a time. ## What should I do if I receive an "Unsupported Format" error? If you encounter an "Unsupported Format" error, double-check that your file is in a supported format. QAEverest.ai currently supports JPEG, PNG, GIF, PDF, DOC, DOCX, XLS, XLSX, and CSV. ## My file is too large. What can I do? If your file exceeds the 20MB size limit, try compressing it. You can also split the file into smaller parts and upload them separately. ## Are there any file types that are not supported by QAEverest.ai? Currently, QAEverest.ai does not support video or audio files. ## Is there a recommended file size for optimal performance? While the maximum file size is 20MB, it's generally recommended to keep your files as small as possible for faster processing times. ## What are the reasons for a QAEverest.ai image upload failure? - Unsupported Format: Ensure your image is in a supported format (e.g., JPEG, PNG, GIF). If it’s not, convert it to one of these formats before uploading. - File Size or Resolution: The image must be under 20MB. If the resolution is too high, reduce it before attempting to upload again. - Network Issues: A slow or unstable internet connection can cause upload failures. Check your connection and try again. - Browser Compatibility: Make sure you’re using an up-to-date browser. Clearing your cache may also help resolve issues. - Retry Uploading: If the upload fails, try selecting a different image or re-uploading the same one after checking the above points ## How do I upload documents to QAEverest.ai? Locate the "Upload" or "Drag & Drop" Section within the QAEverest.ai interface. Click to open a file selection dialog. Choose the document file you want to upload (e.g., PDF, DOCX). Ensure that the document is in a supported format for proper processing. ## How do I upload an image to QAEverest.ai? Use the "Upload" button to select an image file from your device. Supported image formats generally include JPEG, PNG, and GIF. Once selected, confirm the upload to send the image to QAEverest.ai. ## Is QAEverest.ai capable of extracting and analyzing images embedded in PDF, DOC, and DOCX documents? QAEverest.ai currently does not support direct extraction of images from attached PDF, DOC, and DOCX files. Upload images separately. ## Can I upload a PPT to QAEverest.ai? QAEverest.ai currently doesn't specify support for PowerPoint files (.PPT). However, convert your PPT file to a supported format like PDF before uploading. ## What happens if I provide both user stories and design assets? The system will combine the information from user stories and design assets to generate even more comprehensive and accurate test cases. ## What happens if I only provide user stories? If you only provide user stories, the system will generate test cases based on the information contained within the user stories. ## How many credits are deducted for uploading design assets? The system deducts 20 credits per uploaded image. ## Can I cancel the upload and avoid credit deduction? Yes, you can cancel the upload process before clicking "Generate Test Cases" to avoid credit deduction. ## Can I generate testcases from Jira/ClickUp Id? Yes, you can! QAEverest.ai allows you to seamlessly generate test cases directly from Jira or ClickUp IDs by simply entering a valid ID. ## Where can I find the Jira ID API token for my project? Open Jira Account Settings: - Click on your profile icon (usually in the top-right corner). - Select "Account settings" or "Manage account" . Security Settings: - In the left sidebar or under security options, find "Security" or "Personal API tokens" . Create a New API Token: - Click "Create API token" . - Provide a label or name for your token (for example, "Integration for my app"). - Click "Create" and then copy the token. Store the Token Safely: - Make sure to store your API token in a secure location. Jira will not show it again. Use the Token: - Use the token in your API requests by setting it in the Authorization header. ## Where can I find the ClickUp API token for my project? Click on your ClickUp profile → Settings → App ## How to set up Jira/ClickUp integration with QAEverest.ai? - Log in to your QAEverest.ai account. - Navigate to account settings. - Click on "Jira Set Up / Click Up Set Up" to begin the integration process. - Hover and Click on the Jira API Key ## What information is fetched from Jira/ClickUp? The system extracts relevant details such as user stories, descriptions, and attachments associated with the provided ID. ## Can I customize the generated test cases? Yes, you can edit and customize the generated test cases to suit your specific testing needs. ## What formats are supported for downloading test cases? Supports the following formats: Zephyr, TestRail, QTest, QBase, Azure, PractiTest, XLSX, and CSV. ## How do I download test cases? To download test cases, click the "Download Test Cases" button and select your desired format from the dropdown menu. ## What are the available options for test case formats? You can choose between TDD format and BDD format for your test cases. ## How can I view attached files for a test case? To view attached files, go to the test case history page and click the "Attachments" button. A list of attached files will be displayed. ## What types of files can I attach to test cases? You can attach common file types relevant to software testing, such as PDF, DOC, XLSX, CSV, and images. ## How can I download an attached file? To download an attached file, click on the file name in the list. The file will be downloaded to your device for offline reference or further analysis. ## What happens if my Jira/ClickUp task has more than 10 attachments? QAEverest.ai integrates with Jira/ClickUp to help you generate test cases. However, if your Jira/ClickUp task has more than 10 attachments, QAEverest.ai will prioritize and fetch only the top 5 attachments for analysis. ### Story Refinement ## What is Story Refinement? Story Refinement is an AI-powered feature that transforms raw user stories into well-structured, testable requirements. It asks clarifying questions, detects gaps in acceptance criteria, and produces a quality-scored refined story you can immediately use for test case generation. ## How do I access Story Refinement? Click Story Refinement in the left sidebar. The page lists your previously refined stories. Click "+ New Story" to start the 3-step refinement wizard (Story Details → Questionnaire → Refined Story). ## Can I generate test cases directly from a refined story? Yes. After saving a refined story, click "Generate Test Cases" from the story detail view. The refined story's acceptance criteria are used as the input, producing more accurate and comprehensive test cases than a raw user story alone. ## Does refining a story consume credits? Yes. Each refinement uses 1 credit, the same as generating test cases from one user story. For more detailed questions about Story Refinement, see the "Story Creation & Refinement" FAQ tab. ### Generate Test Cases by Postman ## Can I generate test cases from a Postman collection? Yes. QAEverest can analyse a Postman collection and automatically generate API test cases from it. Navigate to Generate Test Cases → By Postman in the sidebar to get started. ## How do I provide my Postman collection? There are two ways: - Upload a file — Export your collection from Postman as a JSON file and upload it directly. - Collection ID — Enter your Postman Collection ID. For organisation users, an admin must configure the Postman API key under integration settings first. ## Where do I find my Postman Collection ID? In Postman, open the collection, click the three-dot menu (…), and select Share . The Collection ID is shown in the share link or in the collection info panel on the right-hand side. ### Generate Test Cases by Swagger / OpenAPI ## Can I generate test cases from a Swagger or OpenAPI specification? Yes. QAEverest reads Swagger/OpenAPI specs (JSON or YAML) and generates comprehensive API test cases covering each endpoint, method, and schema. Navigate to Generate Test Cases → By Swagger in the sidebar. ## How do I provide a Swagger specification? Two options are available: - Upload a file — Upload a .json or .yaml OpenAPI spec from your device. - Swagger URL — Paste the public URL of your Swagger definition (e.g., https://api.example.com/swagger.json ). The URL must be publicly accessible. ## Which OpenAPI versions are supported? QAEverest supports OpenAPI 2.0 (Swagger) and OpenAPI 3.x specifications in both JSON and YAML formats. ### Generate Test Cases by Repository (import an existing automation suite) ## Can I bring my existing automation scripts into QAEverest? Yes. Navigate to Generate Test Cases → By Repository , connect a GitHub, GitLab or Bitbucket repository (read-only), pick a branch and an optional folder, and QAEverest scans it for automation test scripts — Playwright, Cypress, Selenium, WebdriverIO, pytest, JUnit/TestNG, NUnit, RSpec, Go, PHPUnit, Cucumber and Robot Framework. It reads each test together with the page objects and helpers it imports and extracts plain-language QAEverest test cases (steps, expected results, test data, priority, tags) that you review and edit before anything is automated. ## What does a repository import cost? - Connecting, listing branches and the preflight analysis are free. Preflight shows the frameworks and folders detected, an estimated number of test cases and the estimated credits against your balance before you start. - Extraction is billed per test case extracted (1 credit each by default, configurable by the platform admin), deducted file by file as results arrive. If your balance runs out mid-scan the remaining files are skipped — you only pay for what was produced and can re-run later for the rest. - Reviewing, editing and "Proceed to Automation" are free. Running the resulting suites is billed as normal UI / API automation execution. Every import appears in your Credit Log as Repository Import with the repository, branch and number of test cases. ## Where do imported test cases go? After review, Proceed to Automation files the accepted test cases into modules that mirror your repository folders (root = owner/repo ), one suite per test file, in UI, API or Mobile Automation depending on the kind of test. Each file also gets a record in Test Case History. Test cases the extractor was unsure about are flagged with a low confidence score and any helper calls it could not follow, so you can fix or reject them before automating. ### Requirements Traceability (RTM) ## What is the Requirements Traceability Matrix (RTM)? The Requirements Traceability Matrix (RTM) maps each requirement to the test cases that verify it. QAEverest automatically links your stored user stories or Jira/refined stories to generated test cases so you can see which requirements are covered, which are not, and whether linked tests are passing or failing. ## How do I access the Traceability feature? Click Traceability in the left sidebar (marked with a "New" badge). The page displays coverage KPIs, the traceability matrix table, and an AI-powered gap analysis card. ## What is Coverage Gap Analysis? Coverage Gap Analysis is an AI-generated report that highlights requirements with no linked test cases (gaps) and suggests which areas need additional test coverage. It appears as a card on the Traceability page and can be refreshed on demand. ## What information does the RTM table show? For each requirement the table shows: - Requirement title and source (Jira, Refined Story, or User Story) - Risk level (B0 Critical, B1 High, B2 Medium, B3 Low) - Coverage status (Covered / Uncovered) - Linked test run status (Passing / Failing / Not run) - Number of linked test cases ## Does Traceability work with Organisation projects? Yes. If you are part of an organisation you can switch between your personal scope and organisation projects using the project selector on the Traceability page. Only projects you have access to will appear in the dropdown. ## Explore Now! Book a Demo Become a Partner --- # API Test Automation FAQ | QAEverest URL: https://qaeverest.ai/faq/faqapiautomation Summary: How to set up environments, create API test suites, upload test cases, define requests and expected results, run suites and export reports in QAEverest. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # API Test Automation — Frequently Asked Questions ## Why is Environment Setup mandatory before creating a Test Suite? To ensure consistency, accuracy, and controlled execution, QAEverest now requires users to configure an environment before creating a Test Suite. Environments define: • Base URL • Configuration variables • Authentication & authorization details This prevents test failures caused by missing configurations. ## What is an Environment in QAEverest? An Environment is a configuration layer that defines where and how your API tests will run. It includes: • Environment Name (e.g., Dev, QA, Staging, Production) • Base URL • Environment Variables • Authentication & Authorization details ## What happens if I try to create a Test Suite without configuring an environment? The system will: • Prevent Test Suite creation • Display a prompt to configure an Environment first • Redirect you to the Environment Settings page This ensures all test suites are properly linked to a runtime configuration. ## How do I create an Environment? Go to: API Automation → Environment Settings Then: 1. Enter Environment Name 2. Enter Base URL 3. Click Add 4. Configure variables (if required) 5. Configure authentication (if required) Once saved, the environment becomes available for suite creation. ## Can I create multiple environments? Yes, you can create multiple environments such as: • Dev • QA • UAT • Staging • Production Each Test Suite must be linked to one environment. ## How to Create a Test Suite ### Step 1: Access the Suite Creation Page - Navigate to the main dashboard and click on the "+ Create Suite" button. ### Step 2: Fill in Suite Specifications - Title : Enter a descriptive title for your test suite. - Base URL : Input the base URL for the application you are testing. - Environment Name : Specify the environment name if applicable. ### Step 3: Choose Test Case Source - Create New : Click this button to manually enter test case details in the provided text area. - Upload Test Case : Use this option to upload a pre-existing test case file. ### Step 4: Save Your Suite - After filling in all the necessary details, click the "Next" button to proceed. ## Additional Functionalities ### 1. Viewing Test Suite Results - After creating a suite, you can view its results by navigating to the "Test Suites" page. - Each suite displays its status (Passed, Failed), number of tests, percentage of success, and the last run timestamp. ### 2. Managing Test Suites - You can select multiple suites using the checkboxes and perform bulk actions like deletion using the "Delete Selected" button. - Use the search bar to quickly find specific test suites. ### 3. Editing Test Suites - Click on the pencil icon next to a suite to edit its details. ### 4. Cloning Test Suites - Use the clone icon to create a copy of an existing test suite, which can be modified as needed. ### 5. Running Test Suites - The play icon allows you to run a selected test suite. ### 6. Downloading Reports - Click on the download icon to retrieve a detailed report of the test suite results. ### 7. Accessing Suite Logs - The eye icon provides access to the logs for a deeper analysis of the test suite execution. ### 8. Deleting Test Suites - Use the trash bin icon to delete a test suite permanently. ## Troubleshooting ### Why can't I create a new test suite? Ensure all required fields (Title, Base URL) are filled out correctly. ### How do I upload a test case file? Click the "Upload Test Case" button and select the file from your device. ### What formats are supported for test case uploads? Supported formats include JSON, XML, and CSV. ### How can I view the results of a test suite? Navigate to the "Test Suites" page and click on the suite you want to view. ### Can I delete multiple test suites at once? Yes, select the suites using the checkboxes and click "Delete Selected". ## How do I edit a test case in QA Everest? To edit a test case, navigate to the test suite where the test case is located, click on the test case to open it, and then click the pencil icon to edit the details. You can modify the title, steps, and any other relevant information. ## What information is required when creating a new test case? When creating a new test case, you need to provide a title and define the steps for the test case. Each step should include the request method, endpoint, headers, and expected outcomes such as response codes and body content. ## How do I specify the request method and endpoint for a test case? In the test case editor, you can specify the request method (e.g., POST) and endpoint (e.g., products/add/2) under the "Request" tab. This is where you define how the test case will interact with the application. ## What headers should I include in my test case? You should include any necessary headers that are required for the API to process the request correctly. For example, if the API expects a JSON payload, you would include a Content-Type header set to application/json. ## How do I define the expected outcomes of a test case? Under the test case editor, you can define expected outcomes such as the response code (e.g., 201) and the content of the response body (e.g., it should contain "id" and "title"). These expectations are used to validate whether the test case passes or fails. ## Can I add multiple steps to a test case? Yes, you can add multiple steps to a test case by clicking the "+ Add Step" button in the test case editor. This allows you to create complex test scenarios with multiple interactions. ## How do I run a test case? To run a test case, navigate to the test suite containing the test case, select the test case, and click the "Execute Selected" button. This will execute the test case against the specified environment. ## What happens if a test case fails? If a test case fails, it will be marked as failed in the test suite results. You can click on the failed test case to view detailed logs and understand why it failed. ## How can I clone a test case? To clone a test case, click on the test case to open it, and then click the clone icon. This will create a copy of the test case that you can modify as needed. ## Can I delete a test case? Yes, you can delete a test case by clicking on it to open it, and then clicking the trash bin icon. This will permanently delete the test case. ## How do I view the results of a test case? After running a test case, you can view its results by navigating to the test suite and clicking on the test case. The results will show whether the test case passed or failed, along with any relevant logs or error messages. ## Can I export the results of a test case? Yes, you can export the results of a test case by clicking on the download icon next to the test case in the test suite results. This will allow you to save a detailed report of the test case execution. ## Explore Now! Book a Demo Become a Partner --- # Web UI Automation FAQ | QAEverest URL: https://qaeverest.ai/faq/webUI Summary: Answers about QAEverest Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Web UI Automation — Frequently Asked Questions ## What is Web UI Automation in QAEverest? Web UI Automation is an AI-assisted feature that helps you create and execute automated browser-based test scripts for web applications. It can identify UI elements, generate locators, and run end-to-end test flows against a live URL — without requiring manual scripting expertise. ## Why is UI Automation marked as Beta? UI Automation is currently in Beta, meaning the core functionality is available but the feature is still being refined based on user feedback. Some workflows or element types may not yet be fully supported. We recommend using it for evaluation and exploration rather than critical production pipelines at this stage. ## How do I access Web UI Automation? Navigate to Automation → UI Automation in the sidebar. The Beta badge is shown next to the menu item as a reminder of its current status. ## What browsers does UI Automation support? The UI Automation engine targets web-based interfaces via a headless browser. During the Beta period, Chrome-compatible environments are the primary supported target. ## How does element/locator detection work? QAEverest's AI analyses the page DOM and visual layout to identify stable locators for UI elements. It prioritises attributes like data-testid , aria-label , and unique IDs, falling back to CSS selectors or XPath when needed. You can also use the "Find My Locator" tool to inspect any element manually. ## How can I report issues or provide feedback for UI Automation? Use the bug report form at https://qaeverest.ai/HomeReportABug or email support@qaeverest.com . Please include the URL being tested and a description of the issue. ## Explore Now! Book a Demo Become a Partner --- # Mobile & Desktop Automation FAQ | QAEverest URL: https://qaeverest.ai/faq/mobile Summary: Appium-powered mobile test automation in QAEverest — Android and iOS device farms, device setup, app uploads, test suites, scheduling and troubleshooting. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Mobile Automation — Frequently Asked Questions ## What is Mobile Automation in QAEverest? Mobile Automation lets you create, run, schedule, and manage automated test suites for native, hybrid, and web mobile apps on Android and iOS — powered by Appium under the hood. You can target a local device/emulator or run against real devices on a cloud device farm (BrowserStack, Sauce Labs, or LambdaTest), all from the same dashboard used for the rest of your automation suites. ## How do I access Mobile Automation? Navigate to Automation → Mobile Automation in the sidebar ( /mobileautomation ). The page is organized into four tabs: - Test Suites — create, run, and manage mobile test suites. - Test Data Management — maintain reusable test data records. - Automation Scheduler — schedule runs or trigger them via CI/CD webhooks. - Device Setup — configure the device, app binary, and Appium server used for runs. ## What platforms and device farms are supported? Both Android and iOS are supported. For where the tests actually run, you can choose: - Local device / emulator — driven by an Appium server running on your own machine. - BrowserStack — real devices in the cloud. - Sauce Labs — real devices in the cloud. - LambdaTest — real devices in the cloud. When you connect a cloud farm successfully, QAEverest loads its live device catalog so you pick a Platform , Device Name , and OS Version from dropdowns instead of typing them by hand. At a glance, Mobile Automation runs on real-device clouds like BrowserStack , Sauce Labs , and LambdaTest : BrowserStack Real-device cloud Sauce Labs Real-device cloud LambdaTest Real-device cloud ## What tools power Mobile Automation under the hood? Execution is built on Appium , using the uiautomator2 driver for Android and the xcuitest driver for iOS. For a local run, this relies on standard platform tooling — adb for Android and Xcode for iOS — being installed on your machine. Cloud farm runs skip all of that: your app is uploaded to the provider automatically at run time and no local Appium server is required. ## What do I need before I can run a mobile test? - At least one Environment configured for Mobile Automation. If none exists, QAEverest shows an "Environment Settings Required" prompt when you try to create a suite or open Test Suites. - A saved Device Config under the Device Setup tab, with a successful Test Connection result. - Your app binary (APK / IPA / AAB) uploaded or referenced by URL. ## How to Configure a Device for Testing ### Step 1: Open Device Setup - Go to the Device Setup tab and click "⚙️ Configure Device / APK" . ### Step 2: Choose a Device Farm - Select Local device / emulator , BrowserStack , Sauce Labs , or LambdaTest . This choice determines what the rest of the form asks for. - For a cloud farm, enter your Username and Access Key . Each farm links directly to where those credentials live: BrowserStack under Account → Profile → Automate, Sauce Labs under User Settings, and LambdaTest under Profile → Account Settings. ### Step 3: Select Platform, Device, and OS Version - Choose Android or iOS . - Pick the Device Name — from a live catalog dropdown once a cloud farm connects successfully, or typed manually otherwise. Manual entries for cloud farms must match the provider's exact catalog name (e.g. "Google Pixel 7", "iPhone 14"). - Pick the OS Version for the selected device. ### Step 4: Upload Your App - Click "Upload APK/IPA" and select your app file ( .apk , .ipa , or .aab ). It's uploaded once and the resulting URL is filled in automatically — or you can paste a GCS URL / local path directly instead. ### Step 5: Set Up Appium (Local Devices Only) - Enter the Appium Server URL (defaults to http://localhost:4723 ) and click Check to verify it's reachable, or Start Appium to launch it. The built-in setup guide (ⓘ) walks through: - Install Node.js (18+), then Appium: npm install -g appium - Install the platform driver — Android: appium driver install uiautomator2 , iOS: appium driver install xcuitest - Install the platform SDK tools (Android Studio or Xcode). - Confirm your device/emulator is visible: adb devices - Start the server: appium --port 4723 Cloud farm configurations skip this step entirely — the hub endpoint is resolved automatically. ### Step 6: Test Connection and Save - Click Test Connection to validate your credentials, device, and app path. For cloud farms, a successful test also loads the live device catalog. - Once the connection succeeds, click Save Device Config . Save Device Config stays disabled until a Test Connection has passed. ## What app file formats and sizes are supported? APK, IPA, and AAB files are supported, up to 600 MB per upload. ## How do I create and run a Mobile Test Suite? From the Test Suites tab, click "Create Suite" . QAEverest checks your plan and confirms a Mobile environment exists, then opens the suite builder where you can write test case steps directly or upload an existing test case file. Once saved, the suite runs against the device configuration you saved under Device Setup. ### Managing Your Mobile Test Suites - Search — use the search bar to quickly find a suite. - Bulk actions — select multiple suites with checkboxes and delete them together. - Edit — click the pencil icon to update a suite's details. - Clone — duplicate an existing suite to modify as needed. - Run — click the play icon to execute a suite. - Download report — retrieve a detailed report of the run. - View logs — the eye icon opens execution logs for deeper analysis. - Delete — remove a suite permanently with the trash icon. ## How does Test Data Management work for Mobile? Test Data Management lets you define custom record types with your own fields and reuse them across test cases. It's environment-aware — records are tagged with badges like Dev , QA , Staging , or Production . Fields named things like app_path , apk , ipa , or aab automatically get an upload control so you can attach an app binary directly to a record, and fields that look like passwords, secrets, tokens, or PINs are masked. ## Can I schedule Mobile test runs or trigger them from CI/CD? Yes, from the Automation Scheduler tab. When creating a schedule, choose a Trigger Type : - Scheduled (cron) — run the suite hourly , daily , or weekly at a chosen time, or as a one-time run at a specific date and time. - Webhook (CI/CD) — get a unique URL under the "CI/CD Webhooks" tab; POST to it from GitHub Actions, Jenkins, GitLab CI, or any pipeline to trigger the suite. Each run's status (pending, running, completed, or failed) is tracked, and the Run History tab shows results from every scheduled or webhook-triggered run. ## What are common use cases for Mobile Automation? - Regression testing a mobile app across a matrix of Android and iOS devices. - Smoke-testing a new build on real cloud devices before release. - Running nightly or hourly scheduled suites to catch regressions early. - Gating a CI/CD pipeline with a webhook-triggered suite on every build. - Sharing reusable test data (accounts, app binaries, tokens) across environments. ## Best practices for Mobile Automation - Re-run Test Connection any time you change the device farm, credentials, device, or Appium server — a prior successful test is invalidated automatically. - For cloud farms, use the provider's exact device catalog name rather than a free-text guess to avoid run failures. - Keep local Appium and its platform drivers ( uiautomator2 , xcuitest ) up to date to match your target OS versions. - Organize test data by environment (Dev/QA/Staging/Production) so the same suite can run cleanly against different builds. - Use a Webhook trigger instead of a fixed schedule when you want mobile tests to run on every commit or deployment. ## Troubleshooting ### Why does "Test Connection" fail? Double-check your device farm credentials, that the device name/OS version match the provider's catalog exactly, and that your app path is set. For a local setup, make sure the Appium server URL is correct and the server is actually running. ### Why is my local Appium server "not reachable"? Click Check next to the Appium Server URL to re-verify, or click Start Appium to launch it from the app. If it still fails, confirm Appium is installed ( npm install -g appium ) and that the port in the URL matches the port Appium is running on. ### Why can't I pick a device from a dropdown? Device, and OS version dropdowns are populated from a live catalog, which only loads after a successful Test Connection to a cloud farm. Until then — or for local setups — type the device name and OS version manually. ### Why is "Save Device Config" disabled? A successful Test Connection is required before saving. Also make sure the app path and device name are filled in, and — for cloud farms — that your username and access key are set. ### Why does clicking "Create Suite" just show a prompt instead of opening the suite builder? You need at least one environment configured for Mobile Automation first. If none exists, QAEverest shows an "Environment Settings Required" prompt instead — configure an environment, then click "Create Suite" again. ## How can I report issues or provide feedback for Mobile Automation? Use the bug report form at https://qaeverest.ai/HomeReportABug or email support@qaeverest.com . Please include the device farm, platform, and device/OS version you were testing against. ## Explore Now! Book a Demo Become a Partner --- # Performance & Security Testing FAQ | QAEverest URL: https://qaeverest.ai/faq/performance Summary: Run load, stress, spike and soak tests plus header, SSL/TLS and vulnerability scans in QAEverest — metrics reported, credit cost, and risk levels explained. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Performance & Security Testing — Frequently Asked Questions ## Performance Testing ### What is Performance Testing in QAEverest? Performance Testing lets you run real load simulations against any HTTP endpoint directly from QAEverest. It measures how your application behaves under varying levels of traffic and reports key metrics such as response time, throughput, and error rate. ### What types of performance tests are available? - Load Test — Steady concurrent load over a fixed duration. Ideal for baseline benchmarking to see how your system handles expected traffic. - Stress Test — Gradually escalates virtual users until the system breaks. Finds your capacity limit and how gracefully the system degrades. - Spike Test — Sudden burst of traffic then back to baseline. Measures resilience and how quickly the system recovers from unexpected load spikes. - Soak Test — Long-running steady load over an extended period. Reveals memory leaks, connection pool exhaustion, and response drift over time. ### How do I create a performance test run? - Navigate to Performance in the sidebar. - Click "+ New Test" . - Enter the target URL, select a test mode (Load / Stress / Spike / Soak), and configure parameters such as virtual users and duration. - Click "Run" . Results appear on the same page when the test completes. ### How many credits does a performance test consume? Each performance test run consumes 10 credits regardless of duration or test type. ### What metrics does a performance test report? - Average, minimum, and maximum response time - Requests per second (throughput) - Error rate and HTTP status code breakdown - Virtual user ramp-up/down profile - Test start time and total duration ### Can I view previous performance test results? Yes. All past runs are listed on the Performance page. Each entry shows its mode, status, run timestamp, and summary metrics. Click any run to see the full detailed report. ### Can I delete a performance test run? Yes. Click the trash icon next to any run on the Performance page to permanently delete that run and its results. ## Security Testing ### What is Security Testing in QAEverest? Security Testing scans a target URL for common vulnerabilities, misconfigurations, and weaknesses. It provides an automated audit report categorising findings by risk level (Critical, High, Medium, Low, Info) so you can prioritise remediation. ### What scan types are available? - Header Scan — Checks HTTP response headers for security misconfigurations such as missing Content-Security-Policy, X-Frame-Options, or HSTS headers. - SSL/TLS Scan — Validates your SSL certificate, checks for weak cipher suites, protocol versions (TLS 1.0/1.1), and certificate expiry issues. - Vulnerability Scan — Identifies common web vulnerabilities including exposed admin paths, insecure CORS settings, and outdated software signatures. - Full Audit — Runs all scan types together and produces a consolidated risk report across headers, SSL, and vulnerabilities. ### How do I run a security scan? - Navigate to Security in the sidebar. - Enter the target URL you want to scan. - Select the scan type (Header, SSL/TLS, Vulnerability, or Full Audit). - Click "Run Scan" . The findings will appear once the scan completes. ### How are security findings categorised by risk? Each finding is assigned a risk level: - Critical — Immediate action required; actively exploitable. - High — Serious risk; should be addressed promptly. - Medium — Moderate risk; plan remediation. - Low — Minor risk; address as part of routine hardening. - Info — Informational; no direct risk but worth noting. ### Can I view past security scan results? Yes. All previous scans are listed on the Security page with their scan type, status, and issue count summary. Click any scan entry to view the full findings detail. ### Does security testing work on private or internal URLs? Security scans require the target URL to be publicly accessible. Internal or localhost addresses are not reachable from QAEverest's scan engine. ## Explore Now! Book a Demo Become a Partner --- # User Story Creation FAQ | QAEverest URL: https://qaeverest.ai/faq/story Summary: How QAEverest Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # User Story Creation & Refinement — Frequently Asked Questions ## What is Story Refinement in QAEverest? Story Refinement is an AI-powered feature that helps you improve and structure raw user stories into well-defined, testable requirements. It takes your rough story description, asks clarifying questions, and produces a refined story with clear acceptance criteria, role definitions, and quality scores. ## How does the Story Refinement wizard work? The wizard follows a 3-step process: - Step 1 — Story Details: Enter the raw story title, description, role, and any relevant context. You can also import from Jira, ClickUp, or upload a file. - Step 2 — Questionnaire: The AI asks targeted clarification questions to fill in gaps. Answer them to help the AI understand the requirements better. - Step 3 — Refined Story: Review the AI-generated refined story with acceptance criteria, quality score, and variant options. Save or export when satisfied. ## Can I generate test cases directly from a refined story? Yes. After saving a refined story, use the "Generate Test Cases" option from the story detail view. The refined story's acceptance criteria are used as the AI input, producing more accurate and comprehensive test cases than a raw user story alone. ## What is the Story Quality Health Meter? The Story Quality Health Meter is a visual indicator that scores the quality of your refined story across dimensions such as clarity, completeness, and testability. A higher score means the story is ready for test case generation with fewer gaps. ## Can I compare the raw story with the refined version? Yes. Step 3 includes a Raw vs Refined diff view toggle that lets you side-by-side compare what changed between your original input and the AI-refined output. This helps you verify that no important details were lost or altered. ## What are AI story variants? The AI generates up to 3 alternative versions of the refined story. You can browse these variants and pick the one that best fits your requirements. Each variant may emphasize different aspects of the functionality or present acceptance criteria differently. ## What does AC conflict or gap detection mean? QAEverest automatically analyses the acceptance criteria (AC) for logical conflicts (two criteria that contradict each other) and gaps (important scenarios not covered). Warning badges are shown inline so you can address issues before generating test cases. ## Can I import a story from Jira or ClickUp for refinement? Yes. On Step 1 you can paste a Jira issue ID or ClickUp task ID. QAEverest will fetch the story details (title, description, attachments) and pre-fill the form automatically. You can also upload a file (DOCX, PDF, or Confluence export) to import story content. ## Can I export a refined story back to ClickUp? Yes. On Step 3 there is a ClickUp one-click export button. Clicking it pushes the refined story and acceptance criteria back to the linked ClickUp task. Ensure your ClickUp integration is configured under Account Settings before using this feature. ## How many test cases will my refined story generate? Step 3 includes a test-case count estimator that predicts roughly how many test cases will be produced based on the number of acceptance criteria and complexity of the refined story. This helps you plan credit usage before generating. ## How do I save a refined story? On Step 3, click the "Save" button to store the refined story to your account. Duplicate stories are detected automatically — if an identical story already exists you will be warned before saving. Saved stories appear in your Story Refinement History page. ## Where can I view my past refined stories? Navigate to Story Refinement in the sidebar. All previously refined and saved stories are listed there with their quality scores and refinement dates. ## Does refining a story consume credits? Yes. Each story refinement uses 1 credit from your plan balance, similar to how generating test cases from a user story consumes credits. ## Explore Now! Book a Demo Become a Partner --- # UI Automation Setup Guide | QAEverest URL: https://qaeverest.ai/faq/uiautomation-guide Summary: Sample Gherkin and TDD test cases, a locator and self-healing glossary, and best practices for writing steps, setting a base URL and debugging failed runs. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # UI Automation Setup Guide — Complete Reference Sample test cases, glossary, and best practices to ensure your UI automation runs successfully every time. Jump to: Sample Test Cases Glossary Best Practices ## Sample Test Cases Copy these examples directly into the Create Suite editor. Replace URLs, field names, and values with your application's specifics. 1. Login Flow (Gherkin / BDD) ▾ A standard end-to-end login test. Uses fill , click , and assert actions. Feature: User Login Scenario: Successful login with valid credentials Given I navigate to "https://your-app.com/login" When I enter "user@example.com" in the "Email" field And I enter "SecurePass123" in the "Password" field And I click the "Sign In" button Then I should see "Welcome back" And I should see the dashboard TIP Use the exact visible label of the field (e.g. Email , not email_input ). The AI matches by visible text and placeholder, not by HTML attribute names. 2. Registration Form with Data Table (Gherkin) ▸ 3. Navigation and Page Assertion (Gherkin) ▸ 4. Dropdown, Checkbox and Date Picker (Gherkin) ▸ 5. TDD Format (Steps with Expected Results) ▸ 6. Parameterised / Outline Test (Gherkin) ▸ 7. Advanced Actions — Hover, Double-Click, Key Press ▸ ## Glossary Key terms used throughout the UI Automation system. BDD Behavior-Driven Development. Tests written in natural language (Given / When / Then) so both technical and non-technical stakeholders can read them. Gherkin The language used for BDD test cases. Keywords: Feature , Scenario , Given , When , Then , And , But . TDD Format Test-Driven Development style input — numbered steps with an Expected Result section. Detected automatically from Steps: or Test Case ID: headers. Locator A selector that uniquely identifies a UI element on the page. Can be an XPath expression or CSS selector. The AI resolves these automatically. XPath XML Path Language — an expression used to locate elements in the page DOM. Example: //button[text()='Sign In'] . CSS Selector A pattern that matches elements using CSS syntax. Example: button.btn-primary . Faster than XPath in most browsers. data-testid A stable HTML attribute added by developers specifically for test automation. Example: data-testid="login-button" . The AI prioritises these. Stage D (DOM-first) The first stage of locator resolution — it searches the live DOM semantically (label, placeholder, aria, visible text) before using AI vision. Free, fast, and always runs first. JIT Locator Just-In-Time locator resolution. The AI resolves each element's locator immediately before that step executes, ensuring it is resolved against the correct live page. Self-Healing When a step fails because the locator is stale, Agent 6 automatically re-resolves the element and retries the step. Healed locators are cached for future runs. Locator Cache A persistent store of previously resolved (and validated) locators. Cache hits skip AI calls, making re-runs faster and cheaper. Compound Login A special action that fills username, password, and clicks submit in a single step. Triggered by phrases like "log in with email X and password Y" . Assert / Verify A step that checks a condition without interacting with the page. Example: "I should see 'Welcome'" . Does NOT click or type anything. Base URL The root URL of the application under test (e.g. https://staging.your-app.com ). Set in Environment Settings before running suites. Test Suite A named collection of test cases grouped for a specific feature or flow. Suites are run against an environment and produce a pass/fail report. Smoke Test A minimal set of critical-path tests that verify the app is basically functional. Run after every deployment to catch major regressions fast. ## Best Practices Follow these guidelines to maximise run stability and minimise failures. Writing Steps One action per step ▾ Each step should perform exactly one browser action. Combining actions in a single step confuses the AI classifier and produces unreliable results. AVOID When I enter my email, password and click login PREFER When I enter "user@test.com" in the "Email" field And I enter "pass123" in the "Password" field And I click the "Login" button Name elements by their visible label ▸ Quote values and element names ▸ Use 'I should see' for assertions ▸ Environment Setup Always set the Base URL before running ▸ Use a stable, dedicated test environment ▸ App-Side Improvements Add data-testid attributes to your app ▸ Add aria-label to icon-only buttons ▸ Debugging Failures How to read failure reports ▸ Common failure causes and fixes ▸ Quick checklist before running a suite ▸ Need more help? Use the bug report form or email support@qaeverest.com . Include your base URL and a copy of the failing test case for the fastest response. ## Explore Now! Book a Demo Become a Partner --- # Public API FAQ | QAEverest URL: https://qaeverest.ai/faq/public-api Summary: About the QAEverest Test Generator for Jira — API keys, free tier, what Jira data is read, reviewing generated test cases, sub-task creation and security. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # QAEverest Public API & Jira App — Frequently Asked Questions ## What does QAEverest Test Generator for Jira do? It reads the summary and description of the Jira issue you're viewing, sends them to QAEverest's AI service, and generates structured test cases — titles, step-by-step actions, expected results, and priority. You can then push the generated test cases back into Jira as sub-tasks on the same issue. ## How do I get started? 1. Create a free account at qaeverest.ai 2. Go to Settings → API Keys and generate a key (starts with "qae_") 3. Open any Jira issue, launch the QAEverest panel, and paste your API key into Setup 4. Click "Generate Test Cases" ## What is the QAEverest API key, and is it required? Yes — it authenticates your requests to the QAEverest backend and tracks your credit balance. Without it, the app cannot generate test cases. Your key is stored locally in your browser; QAEverest's servers store only a one-way hash of it, never the raw key. ## Is there a free tier? Yes, new accounts include a number of free generations. After that, you'll need an active paid plan on qaeverest.ai — the panel shows your remaining credit balance and prompts you to top up when you run out. ## What Jira data does the app access? Only the summary and description of the issue you actively open the panel on, read on demand — the app does not continuously scan or bulk-export your Jira issues, and does not use your issue content to train AI models. ## Where is my issue content processed? Your submitted issue text is sent from Atlassian's infrastructure to QAEverest's backend (api.qaeverest.ai) and its AI sub-processor to generate a result. See the full Privacy Policy for details. ## Can I review test cases before they're added to Jira? Yes — generated test cases are shown in the panel first. You choose which ones to push; nothing is written to Jira automatically. ## How are test cases added to Jira? Each approved test case is created as a Sub-task under the issue you generated it from, including steps, expected result, and priority in the description. ## What if generation fails or times out? Generation runs as a background job and the panel polls for the result. If it returns an error, check that your API key is valid and that you have remaining credits. Persistent failures can be reported to support. ## Does this work with Jira Service Management? The panel works on any Jira issue type, including service desk requests. ## Is my data secure? All traffic to the QAEverest backend uses TLS encryption. The app only requests the minimum Jira scopes needed (read:jira-work, write:jira-work). ## Where can I get support? Email support@qaeverest.com , or see the Terms of Service and Privacy Policy . ## Explore Now! Book a Demo Become a Partner --- # Accessibility Testing FAQ | QAEverest URL: https://qaeverest.ai/faq/accessibility Summary: Scan pages against WCAG 2.1 and 2.2 levels A, AA and AAA — the six scan types, how the 0-100 score works, AI remediation advice, scheduled monitors, credits. Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Accessibility Testing — Frequently Asked Questions ## What is Accessibility Testing in QAEverest? Accessibility Testing scans your web pages for WCAG 2.2 & AAA accessibility issues and gives you AI-powered remediation advice. You enter a scan name and a target URL, pick a scan type, and QAEverest audits the page against the Web Content Accessibility Guidelines, reporting findings, an overall impact, an issue count, and a score from 0–100. ## Which accessibility standards does it check against? Scans cover the Web Content Accessibility Guidelines WCAG 2.1 and WCAG 2.2 across all three conformance levels — A , AA , and AAA . Every finding is stamped with its WCAG success-criterion number, its conformance level, and the WCAG version it comes from, so you know exactly which guideline it maps to. ## What scan types are available? - ARIA & Semantics — Checks lang, title, landmarks, heading hierarchy, tabindex and ARIA attributes. - Images & Media — Verifies alt text on images, captions on video, and audio controls. - Forms & Navigation — Audits form labels, button names, link text and keyboard navigation. - WCAG 2.2 & AAA — Latest 2.2 criteria such as target size, dragging, accessible authentication and redundant entry, plus AAA enhanced checks. - Contrast & Focus — Renders the page in a real browser to measure colour-contrast ratios and map keyboard focus order as a heatmap. - Full Audit — Everything combined: every check plus WCAG 2.2 & AAA criteria, all in one comprehensive audit. ## How do I run an accessibility scan? - Open Accessibility from the sidebar. - Choose one of the six scan-type cards (Full Audit is selected by default). - Enter a unique Scan Name and a valid Target URL (including http:// or https:// ). - Click Run . Results appear as a "Latest Scan" panel when the scan completes. ## How is the accessibility score calculated? Each scan produces a score from 0–100 . It starts at 100 and deducts points for every issue weighted by severity — critical issues cost the most, then serious, moderate, and minor. The result also shows the overall impact and a conformance breakdown across WCAG levels A, AA, and AAA. ## What does the AI Accessibility Analysis add? After a scan you can click Analyze to get an AI-generated verdict (pass, warning, or critical), a short plain-language summary, key findings, and concrete remediation recommendations — turning raw findings into an actionable fix list. ## Can I schedule recurring accessibility scans? Yes. You can create Scheduled Monitors that re-run a scan automatically on an Hourly, Daily, or Weekly schedule, with an "Alert below score" threshold. Monitors can be paused, resumed, run immediately, or deleted, and each automated run is saved to your scan history. ## How many credits does an accessibility scan consume? Each accessibility scan consumes 10 credits . Scheduled monitor runs cost the same 10 credits per run. Credits are checked before the scan starts, and both personal and organisation balances are supported. ## Can I view my past accessibility scans? Yes. Every scan is saved to your Scan History , showing the name, type, URL, score, issue count, impact, status, and timestamp. Click any entry to view the full findings detail, or delete scans you no longer need. ## Explore Now! Book a Demo Become a Partner --- # Requirements Traceability FAQ | QAEverest URL: https://qaeverest.ai/faq/traceability Summary: How QAEverest Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Requirements Traceability — Frequently Asked Questions ## What is the Traceability feature in QAEverest? Traceability is a Requirements Traceability Matrix (RTM) that links every requirement to the test cases that verify it and shows their latest result — "every requirement → the tests that verify it → their latest result." It gives you a single view of coverage, highlights high-risk gaps, and adds AI gap analysis and PR-scoped smart test selection. ## Where do the requirements in the matrix come from? The matrix pulls requirements automatically from your existing QAEverest data — there is no separate import step: - Refined Stories from Story Refinement (referenced as SR-XXXX ). - Generation Records created when you generate test cases (referenced as REC-XXXX ). - Jira Issues you have imported, shown with their issue key (e.g. QA-17 ). Links are stored in a separate, non-invasive collection, so nothing is changed in your existing stories or records — the mapping is fully reversible. ## What does the traceability matrix show? The Matrix tab opens with four rollup cards — Requirements Covered % , Covered & Passing % , High-Risk Uncovered , and Total Requirements . Below them, requirements are grouped by module (weakest coverage first) in a table with these columns: - Requirement - Risk (business-risk tier) - Linked Tests - Coverage (Covered / Uncovered) - Last Run (Passing / Failing / Not run) - Actions Any uncovered requirement gets a Generate button that jumps into the test generation flow with the requirement pre-filled. ## What are the business-risk tiers (B0–B3)? Each requirement is assigned a risk tier — B0 Critical , B1 High , B2 Medium , or B3 Low . The tier is derived from the business risk of its linked test cases (worst case wins) or falls back to the story's priority. The High-Risk Uncovered card counts B0/B1 requirements that have no tests at all — your most important gaps. ## What is in the per-requirement coverage report? Clicking a matrix row opens a full-page Requirement Coverage Report showing summary KPIs (coverage, linked test count, last run, business risk), the acceptance criteria with an estimated criterion-to-test keyword match, the requirement story text, the trace lineage, and a Linked Tests table (test, status, suite, risk) with a coverage health bar, status filter, and search. If the requirement is uncovered, a coverage-gap callout offers a "Generate tests for this" shortcut. ## What is Smart Selection for GitHub PRs? The Smart Selection tab lets you paste a GitHub Pull Request URL and get back the minimal, risk-ranked set of tests to run for that change. It reports how many tests to run, the percentage of risk covered, the estimated minutes saved versus the full suite, the touched requirements, and any changed files it could not map to tests (RTM blind spots). You can tick tests and run the selected set directly. ## What does AI Gap Analysis do and what does it cost? Run AI Gap Analysis produces an AI narrative for your coverage — a verdict (critical, warning, or pass), a summary, the top risk-ranked uncovered gaps, and recommended actions. If the AI service is offline it falls back to a deterministic heuristic narrative. Gap analysis costs 2 credits per analysis . ## Which traceability actions are free? Viewing the matrix and per-requirement reports is free. Suggesting coverage links with AI, accepting or rejecting suggested links, manually linking or unlinking tests, and running Smart Selection on a PR are all free today. Only the AI Gap Analysis (2 credits) is credit-gated. ## Explore Now! Book a Demo Become a Partner --- # Exploratory Testing FAQ | QAEverest URL: https://qaeverest.ai/faq/exploratory Summary: QAEverest Skip to main content Generate Testcases Story Refinement API Automation Web UI Automation Mobile Automation Performance & Security UI Automation Guide Public API Accessibility Traceability Exploratory AI Agent Testing # Exploratory Testing — Frequently Asked Questions ## What is Exploratory Testing in QAEverest? QAEverest's Exploratory Testing is an Autonomous Exploratory Tester . You point it at a URL (with optional credentials) and walk away — it logs in, crawls the live app, generates test cases, runs them for real, and files the bugs it finds, with zero test scripts written by you. ## What are the phases of an exploratory run? Each run works through five sequential phases: - Login — opens the URL in a real browser, finds the login form, signs in, and verifies success. Apps with no password field are treated as public and skip straight to crawling. - Crawl — breadth-first crawl of the same origin only, recording pages, links, buttons, inputs, and forms. A destructive-action filter avoids following logout, delete, or cancel links. - Discover Flows — AI synthesizes up to six Gherkin flows from the crawl map, with a deterministic heuristic fallback if AI is unavailable. - Execute — runs each flow in a real browser, reusing the authenticated session. - Triage Bugs — AI analyzes each failed flow into a structured bug candidate. ## How do I start an exploratory run? - Open Exploratory from the sidebar. - Enter the Application URL (must start with http:// or https:// ). - Optionally add a username/email and password for protected apps, and adjust the advanced options — max pages to crawl (1–25, default 8) and max crawl depth (1–4, default 2). - Click Start Autonomous Run . The page shows a live phase stepper, progress bar, and counters for pages crawled, flows generated, passed, failed, and bugs found. ## What does an exploratory run produce? - Discovered flows — each an expandable Gherkin feature tagged as AI- or heuristic-generated, with pass/fail/not-run status. - Execution rollup — passed, failed, and total counts. - Bug candidates — each with a title, severity, expected vs actual behaviour, reproduction steps (the Gherkin), and a screenshot on failure. - Run history — a "Recent runs" list of your last runs with URL, pages, pass/fail, and bug count. ## How are bugs classified, and can I file them? Every bug candidate is given a severity of Critical , High , Medium , or Low . You can file any bug with one click to Jira (using a project key) or ClickUp (using a list ID). Once filed, the card shows the issue key with a link, and the same bug can't be filed twice from one run. ## Is my login information safe? Yes. Credentials are passed securely to the testing agent and are never stored — only a masked username (for example "Ad***") is kept with the run record, and the password is never saved. ## How many credits does an exploratory run consume? Each autonomous exploratory run consumes 5 credits , regardless of how many pages are crawled or flows are generated. You can still view the Exploratory page and your run history without an active plan — only starting a run requires a subscription and sufficient credits. ## Explore Now! Book a Demo Become a Partner --- # QAEverest Certification — get certified in AI-assisted QA URL: https://qaeverest.ai/certificate Summary: A hands-on QA certification across three levels: test design, automation across web, API and mobile, failure triage, traceability and release confidence. Practical assessment, verifiable certificate. Skip to main content QAEverest Certification # Prove you can run QA the way it actually works now Three levels, assessed on work you do in the platform rather than on multiple-choice questions. Covering test design, automation across web, API and mobile, failure triage, traceability and the call on whether you're safe to ship. Contact us Certificate of Achievement QAEverest Certified Professional — Level 2 Verification ID QAE-••••-•••• Issued by QAEverest Three levels Associate, Professional and Automation Architect — each one builds on the last. Hands-on, not multiple choice You are assessed on work you actually do in the platform and the practice sandbox. Verifiable certificate Every pass issues a certificate with a unique ID that anyone can check. Shareable badge Add it to your LinkedIn profile, your CV or your team's capability matrix. The three levels ## Start where you are, not at the beginning Each level has its own assessment and its own certificate. Take them in order, or go straight to the one that matches the work you already do. Level 1 ### QAEverest Certified Associate Manual QA engineers and analysts ≈ 4 hours The foundation: turning a story into reviewed, traceable test cases without letting the tool decide what matters. - Refine a vague story into explicit, testable acceptance criteria - Generate test cases and review them against a coverage bar - Apply naming, granularity and format conventions consistently - Link every case back to the requirement it came from Level 2 ### QAEverest Certified Professional Automation engineers and SDETs ≈ 6 hours Everything in Level 1, plus building and running automation across web, API and mobile — and reading a failure properly when it comes back red. - Build and run UI, API and mobile suites from approved test cases - Diagnose a failure from a replayable trace instead of a screenshot - Work with self-maintaining suites: repairs, flaky quarantine, clustering - Set up environments, test data and scheduled runs Level 3 ### QAEverest Automation Architect QA leads, managers and platform owners ≈ 8 hours The programme level: coverage strategy, release gating, governance and rolling AI-assisted QA out across more than one team. - Design a coverage strategy from a requirements traceability matrix - Gate a release on a defensible confidence score, not instinct - Configure roles, permissions and audit for a regulated environment - Evaluate AI agents and LLM features with drift baselines Syllabus ## What the certification covers Eight subject areas, drawn from the parts of QA that cost teams the most time. Which of them appear in your assessment depends on the level. Story refinement Surfacing ambiguity early and turning a thin ticket into acceptance criteria a team can agree on. Test design & generation Path enumeration, boundaries, negatives and roles — and the review discipline that keeps volume from masquerading as coverage. API automation Building request flows, chaining data between calls, assertions, environments and authorisation. Web & mobile UI automation Codeless suites, locator strategy, cross-browser and device runs, and what to do when the UI moves. Performance, security & accessibility Load and stress profiles, vulnerability scanning and WCAG checks as part of the same pipeline. Failure triage & root cause Reading a replayable trace — console, network, DOM, step timeline — and clustering forty failures into one cause. Traceability & release confidence Requirement-to-test coverage, gap analysis, smart test selection from a diff, and gating a pipeline on a score. Testing AI agents Behavioural contracts, judge models, red-team scenarios and drift baselines for non-deterministic features. How it works ## Four steps from signup to certificate Create a free account Signup includes free credits, so you can work through the syllabus on the real platform rather than a demo of it. Work through the modules Short, self-paced modules per level. Each one ends with a task you complete in your own workspace. Practise in the sandbox Rehearse against the ShopVerse practice application, so you break something safe before you touch anything real. Open the practice page . Sit the assessment A practical assessment per level. Pass, and your certificate is issued with a unique verification ID. Who it's for ## Built for the people doing the testing Manual QA moving to automation A structured route from writing cases by hand to running suites, without needing to learn a framework first. SDETs and automation engineers Formal recognition of the maintenance, triage and coverage work that rarely fits on a CV. QA leads and managers A consistent bar across a team, and a capability matrix you can point at during an audit or a hiring round. Consultancies and partners Certified staff to put in front of clients, plus a shared vocabulary across engagements. Questions ## Before you start Do I need automation experience to start? No. Level 1 assumes you have tested software before but not that you have written automation code. Levels 2 and 3 build on it in order. Is the assessment practical or theoretical? Practical. You complete real tasks in the platform and the practice sandbox — refining a story, reviewing generated cases, building a suite, diagnosing a failed run — rather than answering questions about them. How long does it take? Each level is self-paced, with a rough guide of four to eight hours of focused work depending on the level. There is no fixed schedule and no cohort to wait for. Can my whole team be certified together? Yes. Team and organisation-wide certification, including a shared progress view for managers, is arranged through our team — get in touch and we will set it up. How is a certificate verified? Each certificate carries a unique ID. Anyone holding that ID can confirm the holder, the level and the issue date, so an employer never has to take a screenshot on trust. Does the certificate expire? The platform changes, so certification is revalidated periodically against the current syllabus. You will be told well before anything lapses. ## Get certified on the platform you'll actually use Create a free account, work through the syllabus on real projects, and come away with something you can show — not just something you sat through. Contact us ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # QAEverest Blog — AI in QA, test design and automation URL: https://qaeverest.ai/blog Summary: Field notes from the QAEverest team on AI-assisted test design, self-maintaining automation, root-cause analysis and deciding when you are safe to ship. Skip to main content QAEverest Blog # What we've learned building QA that people actually trust Practical writing on AI-assisted test design, suites that repair themselves, failures that explain themselves — and the awkward parts nobody puts in a product page. All posts Test Design Automation AI & Quality QA Engineering Test Design 14 July 2026 9 min read ## Reduce test design time — while keeping your QA team in control Which part of test design actually collapses, which part shouldn't, and why the second half of that sentence is the harder engineering problem. Read the article Automation 30 June 2026 6 min read ### Your test suite is red again. Nobody's looking. It isn't one problem. It's four — and almost every tool on the market fixes one of them, then hands you the other three. Read more QA Engineering 12 June 2026 5 min read ### A screenshot is not a root cause Why the artefact most test tools hand you at the moment of failure is the one piece of evidence that can't tell you what went wrong — and what replaces it. Read more AI & Quality 28 May 2026 7 min read ### Testing AI agents when the output changes every run Assertion-based testing assumes a deterministic answer. LLM-backed features don't have one. What you can still hold them to — and how to keep the bar from drifting. Read more ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Reduce test design time without losing QA control | QAEverest Blog URL: https://qaeverest.ai/blog/reduce-test-design-time-keeping-qa-in-control Summary: AI collapses the transcription half of test design, not the judgement half. A precise look at where the time goes, what stays human, and how to measure the saving in your own team. Skip to main content Test Design 14 July 2026 9 min read QAEverest Team # Reduce test design time — while keeping your QA team in control Which part of test design actually collapses, which part shouldn't, and why the second half of that sentence is the harder engineering problem. "AI writes your test cases" is an easy promise to make and a hard one to live with. Most QA leads have already seen the failure mode: a tool generates hundreds of test cases from a user story in under a minute, the team spends the next two days reading them, keeps a handful, and quietly stops using the tool. Time saved: negative. So it's worth being precise about which part of test design gets faster — and what deliberately doesn't. ## A note on the missing percentage Every tool in this category, ours included, is tempted to put a number on this. You'll see figures quoted with a confidence that nobody has earned. We're not going to give you one, for a simple reason: the honest answer depends entirely on your stories, your conventions, your domain and your review bar. A team with well-written acceptance criteria and a light review process sees something dramatic. A team working from three-line tickets in a regulated domain sees something real but far more modest. A single percentage flattens that difference into marketing. What we can describe precisely is where the time goes and which part of it disappears. That's more useful anyway, because it tells you whether your team is the kind that will benefit — and there's a way to measure it yourself at the end of this piece. ## What test design actually consists of Pull the task apart and it isn't one activity. It's five, and they cost very different amounts. Step What it is Who's good at it Read and interpret the story Understanding what's being built and what "done" means Human, mostly Enumerate the paths Happy path, alternates, negatives, boundaries, roles, states Machine, genuinely Write each case out Preconditions, steps, test data, expected results Machine, overwhelmingly Apply house conventions Naming, granularity, format, linkage to requirements Machine, once told Judge what matters Which risks are real here, what to cut, what's missing Human, non-negotiable In most teams, the middle three consume the overwhelming majority of the clock. They are enumeration and transcription — mechanical work performed by expensive people. That's the part that collapses. The first step gets easier, because the tool forces the ambiguities in a story to the surface early. The last step doesn't get faster at all, and shouldn't. That's the honest shape of the claim: a reduction in authoring time, not in thinking time. Anyone quoting you a figure that quietly includes the thinking is selling you something. ## The fear is not that AI is bad at this. It's that it's confidently mediocre. The real objection from experienced QA people isn't "the machine can't write a test case." It plainly can. The objection is subtler and better: - It will produce volume that looks like coverage but isn't. - It won't know that this particular payment flow has a regulatory edge case nobody wrote down. - It won't follow our naming conventions, so everything needs rewriting anyway. - Reviewing generated output will cost more than writing it ourselves. - And when a test is wrong, nobody will be able to say why it exists. Every one of those is a control problem, not an intelligence problem. Which is why "keeping your QA team in control" isn't the reassuring clause at the end of a marketing sentence — it's the part that determines whether the saving is real or imaginary. ## What "in control" has to mean, concretely Control is not a checkbox. It's a set of specific properties in the workflow. Ours look like this. Nothing enters a suite without a human saying so. Generation produces a draft. A person reviews it, edits it, deletes what's noise, adds what's missing, and then promotes it. The gate is the default, not an optional strict mode. If a tool auto-commits generated cases into your regression pack, you don't have a QA process, you have a content farm. Everything is editable text, not an opaque artefact. A generated case is a normal test case. You change the steps, the data, the expected result. There's no regeneration penalty, no "the AI owns this file now." The refinement step comes before generation. A vague story produces vague tests — that's true of humans too. Refining the story into explicit acceptance criteria first, with the team correcting it, is where most of the quality comes from. It also surfaces the ambiguity while it's still cheap to fix, which is a benefit that has nothing to do with test cases at all. Your conventions are configuration, not a wish. Granularity, naming, format, how much detail a step carries — these are set once and applied to every generation, so review is about content , not reformatting. The review that kills adoption is the one where a reviewer retypes every title. It reads your context, not just the prompt. Generation grounded in your own requirements, existing suites and prior test cases produces something that looks like it came from your team. Generation grounded in nothing produces something that looks like it came from the internet. Everything is traceable. Each case links back to the requirement it came from. That gives you two things reviewers actually want: the ability to ask "why does this test exist?" and get an answer, and a coverage view that shows which requirements have no tests at all — the gap that manual authoring is worst at spotting. Permissions and audit are real. Who can generate, who can approve into a suite, who can delete, and a record of what happened. In a regulated environment this is the difference between a pilot and a rollout. ## The workflow, end to end - Story in — from Jira, a GitHub branch, a Figma file, a document, or typed straight in. - Refine — ambiguity surfaced, acceptance criteria made explicit, team corrects it. - Generate — cases across the paths, in your format, linked to requirements. - Review — the QA engineer does the part only they can do: cut, sharpen, add the domain edge cases the story never mentioned. - Promote — approved cases become a suite; automation and execution follow from there. The shape of the engineer's day inverts. Most of it currently goes on transcription, and a thin slice on judgement. Afterwards, the transcription is largely handled and nearly all of the day is judgement. That's the actual claim, and it's a better one than raw speed. ## What this does not fix Worth saying plainly, because the limits are where trust is earned: - Exploratory testing is untouched. Nothing here replaces a skilled person poking at a build with intent. Generated cases cover the specified; exploration finds the unspecified. - Undocumented domain knowledge stays undocumented. If the reason a flow is risky lives only in a senior tester's head, the tool won't know it until someone writes it down. Review is where that knowledge enters. - Test data strategy is still yours. Realistic, compliant, environment-appropriate data is a design problem, not a generation problem. - Review time is not zero. It's real, it's the point, and it should be budgeted. Any saving worth talking about is the one left after review — everything else is a demo. ## How to measure it in your own team Since we won't hand you a number, here's how to produce your own. It's a cheap experiment. - Take three stories of ordinary complexity from your last sprint. - Have someone author test cases the usual way. Record wall-clock time, case count, and reviewer time. - Run the same three stories through the generate-and-review path. Record the same three figures — including review. - Compare on three axes: time per approved test case , requirements covered , and, a sprint later, defects those cases actually caught . The axis most tools hope you won't measure Defects caught. Faster production of tests that find nothing is not a win — it's the same problem with better throughput. So the question I'd put to any QA lead reading this: if the transcription went away tomorrow, what would your team do with the attention it freed? The teams that get the most out of this don't use it to write the same tests faster. They use it to finally cover the flows they've been knowingly skipping for two years. ## See it on your own stories 1000+ free credits on signup, no card required — roughly 100 test-case generations before you pay for anything. Start free ## Keep reading Automation 6 min read ### Your test suite is red again. Nobody's looking. It isn't one problem. It's four — and almost every tool on the market fixes one of them, then hands you the other three. Read more QA Engineering 5 min read ### A screenshot is not a root cause Why the artefact most test tools hand you at the moment of failure is the one piece of evidence that can't tell you what went wrong — and what replaces it. Read more AI & Quality 7 min read ### Testing AI agents when the output changes every run Assertion-based testing assumes a deterministic answer. LLM-backed features don't have one. What you can still hold them to — and how to keep the bar from drifting. Read more ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # Why nobody reads your test results any more | QAEverest Blog URL: https://qaeverest.ai/blog/your-test-suite-is-red-again-nobody-is-looking Summary: Authoring cost, maintenance churn, screenshot-only failures and an unanswerable ship decision are one knot, not four problems. Why point solutions disappoint. Skip to main content Automation 30 June 2026 6 min read QAEverest Team # Your test suite is red again. Nobody's looking. It isn't one problem. It's four — and almost every tool on the market fixes one of them, then hands you the other three. It's 9:14 on a Tuesday. The nightly run finished. Forty tests are red. Nobody panics. Somebody says "yeah, that's the new checkout page" and everyone goes back to work. By Thursday the suite is amber. By the end of the sprint, it's furniture. If that feels familiar, here's the thing: it isn't one problem. It's four. And almost every tool on the market fixes one of them, then hands you the other three. ## 1. Writing the tests takes longer than building the feature Someone reads the story. Someone imagines the paths. Someone types them out, one by one, for two days. Then the story changes. And here's the part that stings — those tests describe what we assumed people would do. Meanwhile production is sitting there with a full recording of what they actually do, and almost nobody feeds it back in. ## 2. Then the tests break, and it's never a bug A button moves. Forty tests die. Zero defects found. Every sprint, someone spends a day resurrecting tests that never caught anything. It's the tax nobody puts on the roadmap. A suite you don't trust isn't a slow safety net — it's no safety net, with a maintenance bill attached. ## 3. When something does break, you get a screenshot A screenshot shows you the moment of death. Not the cause. Console output: gone. Network calls: gone. What the app was doing three steps earlier: gone. So an engineer reproduces it by hand — which is precisely the manual testing the automation was supposed to replace. On plenty of teams, triage costs more hours than the fix. And those forty red tests? Usually not forty problems. One problem, forty times. But you'd only know that if something were counting. ## 4. And still nobody can answer the only question that matters "Are we safe to ship?" A green pipeline means the tests you thought to write passed on the paths you thought of. It says nothing about what isn't covered, which tests are flaky, or how risky this change is. So the call gets made on instinct. Which works fine — until the retro where someone says "we didn't have a test for that." Which was knowable. Just not visible. ## Four problems, one knot They're connected, and that's exactly why point solutions disappoint. Write tests faster and you have more tests to maintain. Maintain them better and you have more results to triage. Triage brilliantly and you still can't say whether to ship. So we built QAEverest to take all four at once: - Tests that write themselves from your stories and your real production traffic. - Suites that repair themselves when the UI moves, quarantine flaky tests, and cluster forty failures into one cause. - Failures that explain themselves — every step replayable, with AI reading the run and telling you what actually broke. - A release decision with a number behind it , fusing coverage, stability and results into one score that can gate the pipeline. All of it inside VS Code, JetBrains, Jira and GitHub — because a tool that lives somewhere your developers don't go hasn't solved anything. It won't make QA effortless. It removes the effort that produces nothing: fixing tests that found no bugs, reproducing failures by hand, and guessing at risk. So — which of the four costs your team the most? We'd bet on maintenance, because it hides inside sprint capacity and never shows up on a roadmap. ## See it on your own stories 1000+ free credits on signup, no card required — roughly 100 test-case generations before you pay for anything. Start free ## Keep reading Test Design 9 min read ### Reduce test design time — while keeping your QA team in control Which part of test design actually collapses, which part shouldn't, and why the second half of that sentence is the harder engineering problem. Read more QA Engineering 5 min read ### A screenshot is not a root cause Why the artefact most test tools hand you at the moment of failure is the one piece of evidence that can't tell you what went wrong — and what replaces it. Read more AI & Quality 7 min read ### Testing AI agents when the output changes every run Assertion-based testing assumes a deterministic answer. LLM-backed features don't have one. What you can still hold them to — and how to keep the bar from drifting. Read more ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # A screenshot is not a root cause | QAEverest Blog URL: https://qaeverest.ai/blog/a-screenshot-is-not-a-root-cause Summary: Failure triage costs more than the fix on many teams. What a run has to capture — console, network, DOM, step timeline — for a failure to explain itself. Skip to main content QA Engineering 12 June 2026 5 min read QAEverest Team # A screenshot is not a root cause Why the artefact most test tools hand you at the moment of failure is the one piece of evidence that can't tell you what went wrong — and what replaces it. Ask a QA engineer what they got the last time an automated test failed, and the answer is almost always the same: a screenshot, a stack trace pointing at the assertion, and a line number. All three describe the moment the test gave up. None of them describe why. ## The evidence gap By the time an assertion fails, the interesting part already happened — usually several steps earlier. A token expired. A network call 404'd and the app swallowed it. A modal from the previous test never closed. The screenshot faithfully records the consequence and discards every one of those causes. So the engineer does the only thing left: reproduces it by hand. Which is exactly the manual testing the automation was bought to remove. The quiet cost On a lot of teams, triage costs more hours than the fix. It rarely shows up as a line item, because it's distributed across everyone's afternoon. ## What a run has to capture instead A failure explains itself only if the run recorded enough to reconstruct it. In practice that means four streams, captured continuously rather than at the moment of death: - Console — every log, warning and uncaught error, timestamped against the step that produced it. - Network — requests, statuses, timings and payload shapes, so a silent 500 stops being invisible. - DOM state per step — what the page actually looked like when the step ran, not just at the end. - The step timeline itself — scrubbable, so you can walk backwards from the failure to the last moment everything was fine. With those four, the question changes from "what does this screenshot show?" to "what was true three steps ago that isn't true now?" — which is an answerable question. ## Forty failures, one cause There's a second benefit that only appears at suite scale. Once runs carry structured evidence, failures can be clustered . Forty red tests that all died on the same missing element are one defect, not forty. A triage queue that says so is a queue someone will actually work through. The goal isn't a prettier failure report. It's making the first question after a red run 'which cause?' instead of 'who wants to look at this?' QAEverest records every run as a replayable trace and reads it back with AI to propose a root cause, then groups related failures so the queue reflects problems rather than symptoms. ## See it on your own stories 1000+ free credits on signup, no card required — roughly 100 test-case generations before you pay for anything. Start free ## Keep reading Test Design 9 min read ### Reduce test design time — while keeping your QA team in control Which part of test design actually collapses, which part shouldn't, and why the second half of that sentence is the harder engineering problem. Read more Automation 6 min read ### Your test suite is red again. Nobody's looking. It isn't one problem. It's four — and almost every tool on the market fixes one of them, then hands you the other three. Read more AI & Quality 7 min read ### Testing AI agents when the output changes every run Assertion-based testing assumes a deterministic answer. LLM-backed features don't have one. What you can still hold them to — and how to keep the bar from drifting. Read more ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All --- # How to test AI agents and LLM features | QAEverest Blog URL: https://qaeverest.ai/blog/testing-ai-agents-when-the-output-changes-every-run Summary: Exact-match assertions break on non-deterministic output. Behavioural contracts, judge models, drift baselines and red-team scenarios for testing AI agents. Skip to main content AI & Quality 28 May 2026 7 min read QAEverest Team # Testing AI agents when the output changes every run Assertion-based testing assumes a deterministic answer. LLM-backed features don't have one. What you can still hold them to — and how to keep the bar from drifting. Every QA practice ever built rests on one assumption: run the same input twice, get the same output twice. Break that and most of the toolbox stops working — exact-match assertions, golden files, snapshot tests, the lot. That's the position teams are in the moment an LLM lands in the product. The feature works. It just never answers the same way twice. ## Stop asserting the words. Assert the contract. The output varies. The obligations don't. Almost every AI feature has a set of properties that must hold on every run regardless of phrasing: - Task completion — did it actually do the thing that was asked? - Grounding — is every factual claim traceable to the context it was given, or did it invent one? - Format — valid JSON, required fields present, schema respected. - Refusal boundaries — did it decline what it should decline, and only that? - Tool use — did it call the right tool, with sane arguments, and handle a failure? - Safety and leakage — no system prompt, no other user's data, no credentials. Those are testable on non-deterministic output because they're properties of a response, not a specific string. ## Who grades it Some of those checks are mechanical — schema validation, a regex for a leaked key, a check that a tool was called. Run those first; they're cheap and they never disagree with themselves. The rest need a judge model, and a judge model brings its own two problems. It is itself non-deterministic , and it is itself a target — content in the response can try to talk to it. Two rules that keep a judge honest Freeze the judge (pin the model and the rubric, version them, and treat a judge change as a breaking change), and always pass the candidate response as clearly delimited data, never as instructions the judge could follow. ## Drift is the real failure mode AI features rarely fail loudly. They degrade. A prompt gets tweaked, a model version rolls forward, a retrieval index goes stale — and quality slides a few percent a week until someone notices in a support ticket. The defence is a baseline : a fixed scenario set, scored on every run, tracked over time. What matters isn't the absolute score, it's the delta. A five-point drop against last week's baseline is a signal; a score of 0.83 in isolation is a number. ## Scenarios have to keep coming A hand-written scenario set decays. It reflects what you feared at the time you wrote it, and real users are more creative than that. Feeding production traffic back into the scenario set — the odd phrasings, the multi-turn detours, the prompts that tried to jailbreak it — is what keeps the eval honest a quarter later. Test the properties that must always hold, watch the delta rather than the score, and keep the scenario set growing from real traffic. QAEverest's AI Agent & LLM testing runs black-box evaluation, red-team scenarios and drift baselines against your agent — no access to weights or prompts required. ## See it on your own stories 1000+ free credits on signup, no card required — roughly 100 test-case generations before you pay for anything. Start free ## Keep reading Test Design 9 min read ### Reduce test design time — while keeping your QA team in control Which part of test design actually collapses, which part shouldn't, and why the second half of that sentence is the harder engineering problem. Read more Automation 6 min read ### Your test suite is red again. Nobody's looking. It isn't one problem. It's four — and almost every tool on the market fixes one of them, then hands you the other three. Read more QA Engineering 5 min read ### A screenshot is not a root cause Why the artefact most test tools hand you at the moment of failure is the one piece of evidence that can't tell you what went wrong — and what replaces it. Read more ## Website Uses Cookies We use cookies to ensure you get the best experience on our website. By continuing to browse, you agree to our use of cookies. Read our Privacy Policy Reject All Accept All