foundry-test

Debugging failing pytest tests systematically requires a five-phase investigation workflow that categorizes failures, forms hypotheses, and gathers context for AI-assisted root-cause analysis. This structured process ensures consistent outcomes and reliable fixes.

Investigate failing jest test suites using AI by running the failing tests, categorizing the failures, and feeding the captured context into ready-made AI prompts. This structured workflow guides hypothesis formation and verifies fixes through clear decision rules.

Yes, you can use this structured debugging workflow for Go test failures. The language-agnostic workflow applies to Go test suites alongside Python and JavaScript, providing clear decision rules and context capture for AI-assisted analysis and reliable fixes.

You categorize test failures by running the test suite and identifying the failure types using built-in investigation references during Phase 2. This failure categorization accelerates diagnosis and guides the subsequent hypothesis formation and context gathering phases.

Yes, structured test debugging works without external dependencies. The workflow operates independently using built-in failure categories and investigation references to guide diagnosis, leveraging ready-made prompts for optional AI consultation and knowledge reuse.