Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, 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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, 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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, 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.
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
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.
Hey our bad, the wording is not clear. We do make use of the AST to give the AI more context of the codebase.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, 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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, 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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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.
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
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.