- cross-posted to:
- technology@lemmy.world
- cross-posted to:
- technology@lemmy.world
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.
You must log in or register to comment.
Mutation testing is a cool concept, but what’s it got to do with the fediverse?
On the one hand, mutation testing is an important concept that more people should know about and use.
On the other, I fail to see how AI is helpful here, as mutation testing is an issue completely solvable by algorithms.
The need to use external LLMs like OpenAI is also a big no from me.
I think I’ll stick to Pitest for my Java code.
So mutation testing is able to create mutations that are not created by traditional mutations, these are mutations that are more dependent on contextual understanding of code which LLMs excel at. We do preprocessing on our side where we generate a minimal AST of all covered files and pass it to give the LLM a rich contextual understanding of the codebase, allowing us to generate good mutations. Also we make use of LiteLlm so it completely works with open source models too.
Here is a YouTube video with a more in depth explanation of the benefits of using LLMs for mutation testing https://www.youtube.com/watch?v=8h4zpeK6LOA
Mutahar after reading the name: I’m in danger
Mutahar after reading the description: phew
So you’re saying i have to write tests for my test cases now? Ooof.
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.
Yo dawg, I heard you like tests, so I got some tests for your tests so you can test while you test!
What’s the use case? ELI5.
You have written tests for your code and now feel safe because your code is tested. But test quality is really hard to measure. The idea seems to be to introduce “vulnerabilities” (whatever that means…) and see if your tests catch them. If they do that’s supposed to show that the tests are good and vice versa.
