T-Rec: Fine-Grained Language-Agnostic Program Reduction Guided by Lexical Syntax

作者：Zhenyang Xu, Yongqiang Tian, Mengxiao Zhang, Jiarui Zhang, Puzhuo Liu, Yu Jiang, C. P. Sun · 发表于：ACM Transactions on Software Engineering and Methodology · 年份：2024 · DOI：10.1145/3690631 · 被引用次数：7 · 研究领域：Software Testing and Debugging Techniques、Software Reliability and Analysis Research、Software Engineering Research

Program reduction strives to eliminate bug-irrelevant code elements from a bug-triggering program, so that (1) a smaller and more straightforward bug-triggering program can be obtained, (2) and the difference among duplicates (i.e., different programs that trigger the same bug) can be minimized or even eliminated. With such reduction and canonicalization functionality, program reduction facilitates debugging for software, especially language toolchains, such as compilers, interpreters, and debuggers. While many program reduction techniques have been proposed, most of them (especially the language-agnostic ones) overlooked the potential reduction opportunities hidden within tokens. Therefore, their capabilities in terms of reduction and canonicalization are significantly restricted. To fill this gap, we propose \(\mathsf{T}\) - \(\mathsf{Rec}\) , a fine-grained language-agnostic program reduction technique guided by lexical syntax. Instead of treating tokens as atomic and irreducible components, \(\mathsf{T}\) - \(\mathsf{Rec}\) introduces a fine-grained reduction process that leverages the lexical syntax of programming languages to effectively explore the reduction opportunities in tokens. Through comprehensive evaluations with versatile benchmark suites, we demonstrate that \(\mathsf{T}\) - \(\mathsf{Rec}\) significantly improves the reduction and canonicalization capability of two existing language-agnostic program reducers (i.e., Perses and Vulcan). \(\mathsf{T}\) - \(\mat...