test-evidence-review

To review test file quality before a QA sign-off, you need an automated assessment of assertion coverage, edge case handling, and naming conventions. This process evaluates logic, integration, and UI/Visual stories using content analysis to ensure thorough correctness and completeness.

Assessing evidence document completeness for a specific user story is best handled by running an argument-based review filtered by story path. This evaluates the evidence against quality criteria and generates a summary report to ensure all required documentation is present and accurate.

Yes, you can filter a test evidence review by sprint or system name. The review operates using argument-based filtering, allowing you to target specific sprints or systems directly rather than evaluating individual files, ensuring comprehensive coverage for the entire scope.

Automated test evidence review works without external dependencies by using Python's built-in libraries and grep for text matching. It navigates the file system to analyze test file content, evaluating assertion coverage and logic stories through internal content analysis mechanisms.

Yes, test evidence review supports generating persistent reports for sprint-level analysis. Along with providing a summary directly in the conversation, it can output an optional persistent report file to document the quality assessment of your test files and evidence documents.