test-coverage-improver

To identify test coverage gaps in a Python project, you can analyze existing coverage reports to surface the lowest-coverage modules and branches. This helps engineering teams locate missing tests and plan targeted additions to reduce regression risk.

The best way to generate test ideas for low-coverage code is through automated coverage analysis that prioritizes critical modules and recently changed code. This approach drafts concrete test scenarios and awaits your approval before implementing any actual code changes.

You propose targeted tests for recently changed Python code by consuming coverage reports and prioritizing test ideas for those specific areas. This maximizes impact by focusing on new modifications and drafting actionable test concepts for user sign-off.

Yes, the test coverage analysis workflow requires manual approval before changing code. It operates on an approval-driven model where it documents findings and drafts test scenarios, ensuring user sign-off is obtained before any implementation begins.

You analyze coverage reports to reduce regression risk by running automated coverage analysis from the repository root using make coverage. Reviewing the regenerated report surfaces missing coverage areas and proposes targeted test additions to minimize future regressions.