SimP: Unifying Syntax- and Semantic-Guided Techniques for Efficient Program Reduction

Abstract

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.