AI 中文总结
SimP框架结合传统与LLM式缩减技术，兼顾缩减效率与质量，且LLM成本可忽略，解决编译器漏洞调试中测试程序过大的问题。
AI 中文摘要
编译器漏洞在现代编译器系统中普遍存在，但触发这些漏洞的测试程序往往过大，难以用于实际调试。程序缩减通过在保留原始漏洞触发行为的同时最小化测试程序规模来解决这一问题。现有方法主要依赖基于语法的规则式删除策略，以试错方式迭代移除程序部分内容。这些方法在缩减质量上表现有效，但缩减速度较慢。本文提出SimP程序缩减框架，将传统缩减方法与基于大语言模型（LLM）的语法及语义引导缩减相结合，利用定制提示设计引导缩减过程，并协同融合规则式与LLM式缩减阶段以优化缩减性能。结果表明，SimP在实现相当缩减质量的同时提升了缩减效率，且LLM相关的金钱成本可忽略不计。
英文摘要
Compiler bugs are pervasive in modern compiler systems, but the test programs that trigger them are often too large for practical debugging. Program reduction addresses this by minimizing test program size while preserving the original bug-triggering behavior. Existing approaches mainly rely on syntax-guided, rule-based deletion strategies that iteratively remove parts of the program in a trial-and-error manner. While effective in reduction quality, these approaches suffer from slow reduction speed. This paper presents SimP, a program reduction framework that combines traditional reduction with LLM-based syntax- and semantic-guided reduction. SimP leverages customized prompt design to guide the reduction process. SimP synergistically combines rule-based and LLM-based reduction stages to optimize the reduction performance. The results show that SimP improves reduction efficiency while achieving comparable reduction quality, with negligible LLM monetary cost.
