AI 中文总结
该研究针对现有LLM-based APR方法难以修复多块漏洞的问题，提出MultiFixer多智能体框架，经多基准测试，其在Defects4J等数据集上达到最优修复效果，能有效修复多块漏洞。
AI 中文摘要
自动化程序修复（APR）已从大语言模型（LLMs）中获益良多，但现有的基于LLM的APR方法仍难以应对需要跨多个位置协同修改的多块漏洞。这类漏洞需要仓库级上下文理解、修复顺序调度，以及有效的块级补丁生成与选择。为解决这些挑战，我们提出MultiFixer，一种用于多块修复的新型基于协调者-提议者的多智能体框架。MultiFixer执行工具增强型漏洞分析，构建细粒度修复上下文，通过协调者-提议者架构迭代生成补丁，并应用两阶段补丁优化以确保语法和语义正确性。我们在Defects4J的835个漏洞及三个漏洞基准上评估MultiFixer：在Defects4J上，MultiFixer修复326个漏洞，其中包括62个多方法漏洞和27个多文件漏洞，且在使用相同基础模型的已报告比较中优于现有APR基线；结合Claude-3.5-Sonnet时，MultiFixer修复420个漏洞，在Defects4J上达到新的最优水平。在VUL4J上，MultiFixer修复24个真实世界漏洞，其中5个为多块案例；在SEC-bench和PatchEval的多块子集上，MultiFixer分别修复11和19个漏洞，在GPT-3.5下优于所有对比基线。这些结果证明了MultiFixer在多块修复中的有效性。
英文摘要
Automated Program Repair (APR) has benefited greatly from Large Language Models (LLMs), but existing LLM-based APR methods still struggle with multi-hunk bugs that require coordinated changes across multiple locations. These bugs demand repository-level context understanding, repair-order scheduling, and effective hunk-level patch generation and selection. To address these challenges, we propose MultiFixer, a novel Coordinator-Proposer based multi-agent framework for multi-hunk repair. MultiFixer performs tool-augmented bug analysis, constructs fine-grained repair context, iteratively generates patches through a Coordinator-Proposer architecture, and applies two-stage patch refinement for syntactic and semantic correctness. We evaluate MultiFixer on 835 bugs from Defects4J and three vulnerability benchmarks. On Defects4J, MultiFixer fixes 326 bugs, including 62 multi-method and 27 multi-file bugs, and outperforms prior APR baselines in the reported comparisons with the same base model. Moreover, MultiFixer also fixes 46 multi-hunk bugs among 95 unique fixes. When combined with Claude-3.5-Sonnet, MultiFixer repairs 420 bugs, establishing a new state of the art on Defects4J. On VUL4J, MultiFixer repairs 24 real-world vulnerabilities, including 5 multi-hunk cases. On the multi-hunk subsets of SEC-bench and PatchEval, MultiFixer fixes 11 and 19 vulnerabilities, respectively, outperforming all compared baselines under GPT-3.5. These results demonstrate the effectiveness of MultiFixer for multi-hunk repair.
CommentsAccepted to 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
