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.
1 locator auto-healed. All 3 visual assertions confirmed by screenshot.
From Test Case to Verified Result
Three phases, nine agents, zero hardcoded selectors.
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
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 — Shadow DOM and SVG icons included
- Broken selectors auto-healed without manual intervention
- Assertions validated from screenshots, not DOM selectors
- Up to 4 test cases in parallel, with locale, timezone and device emulation
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
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 maintenanceVisual 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 UIRoot 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
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 a suite on Chrome, Firefox, Edge, Chromium or Safari (WebKit). The browser is saved on the suite, so manual runs, schedules and CI/CD triggers all use it.
Parallel Test Execution
Run 2, 3 or 4 test cases at a time, each in its own browser. Login test cases run first and the rest reuse their saved session. One setting per suite covers manual, scheduled and CI/CD runs — and Suite History shows the real elapsed time.
Same credits as a sequential runLocalization Testing
Emulate a locale, a timezone and a device per suite. Check French, German, Hindi, Arabic, Japanese or Chinese pages, region-specific dates and times, and phone or tablet layouts — without changing a single step.
11 locales · 10 timezones · 7 devicesShadow DOM & Web Components
Controls inside open shadow roots — nested components and slotted text included — are found, clicked and asserted through a chained locator with one verified segment per boundary. Closed roots are detected and never guessed at.
Exports to Selenium & Cypress tooSVG & Icon-Only Elements
An icon click resolves to the real button behind the glyph. SVG targets get namespace-safe XPath that is checked against the page before use, with SVG-aware visibility checks — across MUI, react-icons, lucide and Font Awesome icons.
Locators verified before useVisual 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.
4-Stage Locator Resolution
No hardcoded XPath or CSS. Every locator is resolved live from the DOM with a measurable confidence score.
ML Element Detection
Object detection and text recognition scan the page screenshot to identify candidate interactive elements before the DOM is consulted.
AI Vision Matching
AI interprets the screenshot and matches the described element to the most visually accurate candidate identified in Stage 0.
DOM Resolver
Maps fractional screen coordinates to the actual DOM node using elementFromPoint, extracting full HTML context for selector generation.
Locator Output
Generates ranked XPath and CSS selectors — chained for Shadow DOM, namespace-safe for SVG — each with a confidence score.
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.
- 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…”
See It In Action
Write a test case in Gherkin and preview how the AI pipeline executes it in a real browser.
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.