test-coverage-analysis

To validate pytest fixtures and improve test reliability, check fixture integrity during CI workflow reviews by ensuring they properly set up and tear down test environments. The analysis enforces fixture integrity by validating them against project conventions and FROZEN_FACTS.md data to prevent test regressions.

Test coverage analysis suggests new test cases for QA audits by extracting validation data from FROZEN_FACTS.md and matching it against untested critical paths. It generates concrete test recommendations aligned with project conventions to fill coverage gaps in modules like consciousness metrics and basin navigation.

Yes, you can integrate test coverage auditing into existing CI workflows by running the coverage review during pipeline execution to automatically surface gaps. The analysis enforces CI integration by validating pytest fixtures and checking pipeline coverage to prevent regressions before deployment.

The best way to audit test coverage for QIG operations is to run a comprehensive review that identifies untested critical paths across modules like Fisher-Rao distance and consciousness metrics. This approach validates pytest fixtures, suggests cases from FROZEN_FACTS.md, and enforces CI pipeline coverage to prevent regressions.

Your pytest suite may allow regressions because critical paths in QIG operations remain untested even if overall coverage thresholds are met. The analysis identifies these specific untested paths and validates fixture integrity against FROZEN_FACTS.md data to ensure tests reliably catch actual regressions.