mutation-testing

Mutation testing verifies test effectiveness by introducing small changes (mutants) into production code to check if existing tests fail. It identifies weak tests that allow bugs to slip through by analyzing whether code changes are properly detected.

To perform mutation testing analysis, identify changed code on a feature branch, generate mental mutants using mutation operators, verify test coverage against these mutants, and document findings to strengthen weak tests.

High test coverage allows bugs to slip through when tests lack proper assertions or miss boundary conditions. Mutation testing exposes these gaps by altering arithmetic operations and checking if tests actually detect the introduced failures.

Mutation operators are specific code alteration strategies used to generate mutants, such as changing arithmetic operations or boundary conditions. They find weak tests by verifying if the test suite fails when these targeted code changes are introduced.

Yes, you can use mutation testing for branch analysis on a feature branch. It systematically identifies changed code, generates mental mutants for those branches, and verifies if existing tests catch common errors like incorrect boundary conditions.

You should avoid mutation testing when you need to fix active production bugs or lack baseline test coverage. It is designed to verify test effectiveness on stable code changes rather than serving as a primary debugging tool.