mutation-test

The mutation-test skill provides a robust framework for assessing the reliability of your test suite beyond simple line or branch coverage. By automatically introducing semantic code changes—known as mutants—into your source code, this skill determines whether your existing tests can identify and fail upon these artificial bugs. This process acts as a high-fidelity quality gate, ensuring that your test assertions are meaningful rather than just exercising code paths.

Designed for software engineers, QA automation specialists, and developers focused on maintaining high-quality codebases, this skill supports major ecosystems including JavaScript (via Stryker), Python (via mutmut), and Java (via PITest). It is particularly useful when you have high coverage but still experience production regressions, or when preparing a project for rigorous pre-release validation.

• Automatically detects project type and configures mutation testing tools like Stryker, PITest, or mutmut.

• Executes comprehensive mutation analysis to calculate a mutation score, providing a clear metric for test suite effectiveness.

• Identifies surviving mutants that indicate specific areas where test assertions or boundary condition checks are weak.

• Generates a detailed mutation testing report, highlighting file-level scores, survived mutants with diffs, and recommended test improvements.

• Correlates code coverage with mutation scores to uncover the gap between executed code and verified logic.

• Provides actionable feedback to improve test suites by suggesting specific edge cases and error paths for missing tests.

• Input: Triggered by user requests regarding test quality, effectiveness, or debugging of escaped bugs.

• Output: A comprehensive report including a summary table, mutation score, list of survived mutants, and concrete recommendations for test coverage improvement.

• Constraints: Requires a pre-existing test suite (e.g., vitest, pytest, JUnit) to run. Analysis is computationally intensive; runtimes depend on the project size and the number of mutation operators enabled.

• Usage Tip: Aim for a mutation score of 80% or higher as a target for production-ready code. Use the tool's suggestions to prioritize adding tests for surviving mutants in critical business logic or security-sensitive modules.