Turn Your Codebase Into Playwright (and Appium) Tests
AI Repo Analysis reads your application source—not a live browser crawl—maps product features, and authors fresh UI + API tests. Web Playwright, mobile Appium, GitHub or Bitbucket, same heal loop as discovery.
Most teams that need coverage face an awkward choice. Record every flow by hand. Point a crawler at a live staging URL and hope auth, multi-step forms, and dark corners show up. Or hire engineers to write Playwright and Appium for months. All three work. None of them start from the thing you already trust: the source code that defines routes, forms, handlers, and product features.
Validate.QA’s AI Repo Analysis is the missing third ingestion path next to recording, live discovery, and Framework Import . Instead of reading an existing test suite, it reads your application repository — web or mobile — maps product features from the code, and authors fresh tests grounded in real components, routes, and API handlers. Web projects get Playwright UI + API coverage. Mobile projects get Appium step tests for iOS and Android.
This post is the product map: when to use Repo Analysis, how it differs from importing tests or crawling a live site, what you connect, and what lands in your project.
Import Existing Tests vs Analyze App Source
Both live under the same Import tests wizard. The method picker is the fork:
If you already have a solid automated suite, start with Framework Import. If you have a product codebase and little or no E2E coverage — or you want tests that know your real selectors and endpoints from day one — use AI Repo Analysis.
Why Source Beats Black-Box Discovery Alone
Live discovery explores a running app in a real browser (or device). It is excellent at finding what a user can click today. It cannot see dead routes behind feature flags, API contracts only used by mobile clients, or the exact form field names buried in a React component until it stumbles onto them.
Repo Analysis flips the lens. The model reads routes, pages, controllers, and schemas from disk. Phase 1 produces a feature map: product features with routes, API endpoints, key source files, and concrete test ideas. Phase 2 spawns a bounded pool of per-feature authoring agents that write tests grounded in that map — labels from components, paths from routers, payloads from handlers — without opening a browser during the analysis pass.
Topics: AI Repo Analysis, Playwright, Appium, Test Generation, Codebase.
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