CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation

摘要

Automated Program Repair (APR) is vital for software maintenance. Despite notable advancements, existing methods still face challenges of insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. We propose CascadeFix, a multi-location automatic repair method via cascading planning and generation. Firstly, to improve the modeling of semantic and structural dependencies among bugs, three types of bug relationships-Use, Copy, and Nearby-are defined to characterize semantic connection, patch reusability, and contextual interference. Then, to address inadequate global repair planning, a cascading repair planning algorithm is designed to effectively cluster strongly correlated bugs and intelligently assign reasonable repair priorities and operations to each cluster, ensuring the rationality and consistency of global repair. Finally, taking clusters as the basic repair units, a cascading patch generation mechanism is proposed to dynamically integrate intra-cluster dependency information and cross-cluster repair knowledge, producing patches that maintain syntactic correctness and semantic consistency under global dependency constraints. Experiments on Defects4J show that CascadeFix resolves 84 multi-location bugs, achieving a 31% improvement over current state-of-the-art methods.