Lampr: Boosting the Effectiveness of Language-Generic Program Reduction via Large Language Models

Program reduction is a prevalent technique to facilitate compilers' debugging by automatically minimizing bug-triggering programs. Existing program reduction techniques are either generic across languages (e.g., Perses and Vulcan) or specifically customized for one certain language by employing language-specific features, like C-Reduce. However, striking the balance between generality across multiple programming languages and specificity to individual languages in program reduction is yet to be explored. This paper proposes Lampr, the first technique utilizing LLMs to perform language-specific program reduction for multiple languages. The core insight is to utilize both the language-generic syntax level program reduction (e.g., Perses) and the language-specific semantic level program transformations learned by LLMs. Alternately, language-generic program reducers efficiently reduce programs into 1-tree-minimality, which is small enough to be manageable for LLMs; LLMs effectively transform programs via the learned semantics to expose new reduction opportunities for the language-generic program reducers to further reduce the programs. Our extensive evaluation on 50 benchmarks across three languages (C, Rust, and JavaScript) has highlighted Lampr's practicality and superiority over Vulcan, the state-of-the-art language-generic program reducer. For effectiveness, Lampr surpasses Vulcan by producing 24.93\%, 4.47\%, and 11.71\% smaller programs on benchmarks in C, Rust and JavaScript. Moreover, Lampr and Vulcan have demonstrated their potential to complement each other. By using Vulcan on Lampr's output for C programs, we achieve program sizes comparable to those reduced by C-Reduce. For efficiency, Lampr takes 10.77\%, 34.88\%, 36.96\% less time than Vulcan to finish all benchmarks in C, Rust and JavaScript, separately.