lipton-mutation-testing

Test effectiveness is evaluated using mutation testing, which injects small faults into source code via mutation operators and measures the percentage of faults your tests fail to detect, calculating a mutation score.

Mutation testing measures test quality by systematically introducing small, realistic bugs into source code using operators like Arithmetic Operator Replacement. It quantifies test suite quality by calculating the percentage of injected faults that tests successfully detect.

You identify test gaps by analyzing surviving mutants, which are injected faults that your tests failed to detect. This survivor analysis highlights specific areas where tests are lacking, prompting developers to write more robust tests covering edge cases.

Yes, mutation testing analyzes the quality of tests for provided Python source code. By injecting faults, it checks if your tests cover edge cases and boundary conditions, with scores below 80% highlighting specific areas lacking robust tests.

To improve test quality when dealing with surviving mutants, you should follow the provided strategies for writing more robust tests. Analyzing these surviving mutants indicates weaknesses in the test suite and guides improvements for edge case coverage.

High code coverage results in undetected faults because coverage only measures executed code, not test thoroughness. Mutation testing addresses this by injecting realistic bugs to measure test detection rates, revealing test gaps that simple coverage misses.