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Verify

Test cases written by AI from your stories, with traceability that maintains itself.

AI test generation that produces Gherkin test cases, defect predictions, and traceability matrices.

Test management as a separate discipline (TestRail + Jira + spreadsheets) was a workaround for tools that couldn't see across stories and tests. Stride generates test cases from AC at story-creation time, maintains the traceability matrix automatically, and predicts which areas are likely to regress.

Why do AI-generated tests become flaky regression noise without AC traceability?

QA teams spend 30-50% of their time on test administration: writing tests from stories (often a half day per epic), updating the traceability matrix manually, and figuring out which existing tests need to re-run when a story changes. The work is mechanical, the AI is competent at it, and the human time saved goes back into exploratory testing, the work humans are actually better at.

What ties Stride's generated Gherkin tests back to each AC line?

When a story is created with AC, Stride generates Gherkin test cases tied to each AC line. The traceability matrix is the project graph: story → AC → test cases → test runs → defects. When a story's AC change, affected test cases are flagged for review. Defect prediction model surfaces which areas of the codebase are at elevated risk given recent change patterns.

  • Test cases generated from AC in Gherkin format (5-15 per story typical)
  • Test pyramid coverage check: unit / integration / e2e ratio per module
  • Traceability matrix maintained automatically from the project graph
  • Coverage gap detection: AC lines without a corresponding test
  • Defect prediction: surfaces high-risk modules from recent change + complexity patterns
  • Test run analytics: flake detection, slowest tests, regression-test queue prioritisation
Best for

QA-mature teams (5+ QA engineers, formal test management today) who want to reduce admin time and shift to exploratory testing.

Not for

Greenfield projects with no formal QA discipline. The AI works best where there are already AC + a test suite to integrate with. For teams writing tests for the first time, start with /learn/sprint-planning to establish AC discipline first.

Frequently asked

Does the AI run the tests too?
No. Stride generates test cases (as Gherkin or whatever format you use); test execution still happens in your CI (or Playwright / Cypress / pytest runner). What Stride does add: it can ingest test-run results back into the graph so you have one view of 'this test failed on this PR for this story'.
How do I know AI-generated tests are testing the right thing?
Each test case has an explicit link to the AC line it verifies. QA reviews tests at story-acceptance time before they go into the suite. The same workflow you use today for QA review on human-written tests, just with less authoring time up front.
What about non-functional tests (performance, security, accessibility)?
Stride handles functional + acceptance tests well. Non-functional tests (load testing, security scanning, axe-core a11y) are still tool-specific and we don't replace them. The AI can hint at where non-functional tests should be added (high-traffic endpoints, auth flows, public forms) but execution stays in the specialist tool.
How does defect prediction work?
It's a heuristic model trained on: recent commit churn per module, cyclomatic complexity, historical defect density, current PR size. Modules in the top quintile of the score get a 'review carefully' flag at code-review time. It's a probability hint, not a verdict.