mutation-testing-python

Mutation testing in Python verifies your test suite quality by intentionally mutating production code and evaluating if your tests detect the introduced bugs. It reveals whether your tests actually catch defects, going beyond traditional coverage metrics.

To set up Python mutation testing, install mutmut and configure your project using pyproject.toml or setup.cfg. You then run mutation testing alongside pytest to automatically generate mutants and evaluate your test results.

Mutation testing works for validating TDD outcomes and ensuring refactors preserve behavior across changes in Python. It applies when analyzing code on feature branches to confirm your tests catch edge-case gaps and introduced bugs.

Interpreting surviving mutants from mutmut involves identifying which code mutations your pytest suite failed to detect. You analyze these surviving mutants to locate and fix weak tests that lack proper assertions or edge-case coverage.

The best way to run mutation testing incrementally on a feature branch is to apply guided mental mutation analysis and configure mutmut to target only the changed code. This workflow limits scope and provides faster feedback on test quality.

Mutation testing finds surviving mutants despite high code coverage because coverage only measures line execution, not assertion strength. Mutmut reveals tests that execute code but fail to assert correct behavior against introduced bugs.