Syntax Is All You Need: A Universal-Language Approach to Mutant Generation
Sourav Deb, Kush Jain, Rijnard van Tonder, Claire Le Goues, Alex Groce
摘要
While mutation testing has been a topic of academic interest for decades, it is only recently that "real-world" developers, including industry leaders such as Google and Meta, have adopted mutation testing. We propose a new approach to the development of mutation testing tools, and in particular the core challenge of generating mutants. Current practice tends towards two limited approaches to mutation generation: mutants are either (1) generated at the bytecode/IR level, and thus neither human readable nor adaptable to source-level features of languages or projects, or (2) generated at the source level by language-specific tools that are hard to write and maintain, and in fact are often abandoned by both developers and users. We propose instead that source-level mutation generation is a special case of program transformation in general, and that adopting this approach allows for a single tool that can effectively generate source-level mutants for essentially any programming language. Furthermore, by using parser parser combinators many of the seeming limitations of an any-language approach can be overcome, without the need to parse specific languages. We compare this new approach to mutation to existing tools, and demonstrate the advantages of using parser parser combinators to improve on a regular-expression based approach to generation. Finally, we show that our approach can provide effective mutant generation even for a language for which it lacks any language-specific operators, and that is not very similar in syntax to any language it has been applied to previously. CCS Concepts: • Software and its engineering → Dynamic analysis; Software testing and debugging.
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引用它的顶会 Paper4
- Boosting LLMs for Mutation GenerationBo Wang, Ming Deng, Mingda Chen, Chengran Yang 等FSE 2026 · 被引用 1 次
- TestGenEval: A Real World Unit Test Generation and Test Completion BenchmarkKush Jain, Gabriel Synnaeve, Baptiste RozièreICLR 2025
- Efficiently Detecting DBMS Bugs through Bottom-up Syntax-based SQL GenerationYu Liang, Peng LiuNDSS 2026
- FGit: Fault-Guided Fine-Tuning for Code GenerationLishui Fan, Zhongxin Liu, Haoye Wang, Lingfeng Bao 等ASE 2025
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