🤖 AI Summary
Existing automated program repair techniques struggle to address complex logical errors and silent failures due to their inability to accurately model runtime dynamic behaviors and data dependencies. This work proposes TraceRepair, a novel framework that, for the first time, incorporates runtime execution traces as shared constraints within a multi-agent collaboration mechanism. In this approach, a probe agent captures snapshots of critical variables, while multiple specialized agents—powered by large language models—perform cross-validation and iterative refinement to enable precise, dynamic-reasoning-driven repairs. Evaluated on Defects4J, TraceRepair successfully fixes 392 bugs, substantially outperforming current LLM-based methods, and demonstrates strong generalization capabilities on a newly curated dataset of recent vulnerabilities.
📝 Abstract
Automated Program Repair (APR) struggles with complex logic errors and silent failures. Current LLM-based APR methods are mostly static, relying on source code and basic test outputs, which fail to accurately capture complex runtime behaviors and dynamic data dependencies. While incorporating runtime evidence like execution traces exposes concrete state transitions, a single LLM interpreting this in isolation often overfits to specific hypotheses, producing patches that satisfy tests by coincidence rather than correct logic. Therefore, runtime evidence should act as objective constraints rather than mere additional input. We propose TraceRepair, a multi-agent framework that leverages runtime facts as shared constraints for patch validation. A probe agent captures execution snapshots of critical variables to form an objective repair basis. Meanwhile, a committee of specialized agents cross-verifies candidate patches to expose inconsistencies and iteratively refine them. Evaluated on the Defects4J benchmark, TraceRepair correctly fixes 392 defects, substantially outperforming existing LLM-based approaches. Extensive experiments demonstrate improved efficiency and strong generalization on a newly constructed dataset of recent bugs, confirming that performance gains arise from dynamic reasoning rather than memorization.