Check out our open-source, language-agnostic mutation testing tool using LLM agents here: https://github.com/codeintegrity-ai/mutahunter
Mutation testing is a way to verify the effectiveness of your test cases. It involves creating small changes, or “mutants,” in the code and checking if the test cases can catch these changes. Unlike line coverage, which only tells you how much of the code has been executed, mutation testing tells you how well it’s been tested. We all know line coverage is BS.
That’s where Mutahunter comes in. We leverage LLM models to inject context-aware faults into your codebase. As the first AI-based mutation testing tool, Mutahunter surpasses traditional “dumb” AST-based methods. Our AI-driven approach provides a full contextual understanding of the entire codebase, enabling it to identify and inject mutations that closely resemble real vulnerabilities. This ensures comprehensive and effective testing, significantly enhancing software security and quality.
We’ve added examples for JavaScript, Python, and Go (see /examples). It can theoretically work with any programming language that provides a coverage report in Cobertura XML format (more supported soon) and has a language grammar available in TreeSitter.
Check it out and let us know what you think! We’re excited to get feedback from the community and help developers everywhere improve their code quality.
Regarding mutation testing, you don’t write any “tests for your test”. Rather, a mutation testing tool automatically modifies (“mutates”) your production code to see if the modification will be caught by any of your tests.
That way you can see how well your tests are written and how well-tested parts of your application are in general. Its extremely useful.