Tests that get less flaky the more you run them.

A one-off chat with an AI can write you a test. It can't tell you which of your selectors have quietly started failing, or remember what actually held up last time. AccuTest runs your tests for real, tracks every result, and uses that history — not a fresh guess — every time it writes the next one.

How the loop works

1

Upload your requirements

Drop in an ATRD (Automation Test Requirement Document) — plain requirements, no special format needed.

2

Run it for real, repeatedly

GPT writes executable Playwright code, then AccuTest actually runs it against your site and keeps every result — not a one-off.

3

Watch the loop compound

Flaky tests surface automatically from that history, and the next generation for the same site is handed its known-reliable and known-flaky selectors — so it gets better, not just faster.

Built around execution history, not just generation

🔁 Flaky test detection

Every test's pass/fail record across its last 10 runs, surfaced automatically — a rolling flag, not a one-time guess, so a test that's settled down stops being flagged.

🧠 Selector memory

Locators are scored per site from real runs. The next generation for that site is told which have held up and which keep breaking — the one thing a stateless chat prompt can never know.

▶️ Real execution

Generated Playwright tests run for real against a target URL you configure — actual pass/fail, not a simulation.

📊 Dashboard analytics

Real-time stats across sessions, packages, and tasks, all tied back to the requirements they came from.

Also generates across six test domains

⚙️ Functional
⚡ Performance
🔒 Security
♿ Accessibility
🖼️ Visual
🗃️ Data / ETL

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