MultiFixer: A Coordinator-Proposer Based Multi-Agent Framework For Fixing Multi-Hunk Bugs

2026-07-29

AI Summary

This work addresses the challenge that existing large language model–based program repair approaches struggle to effectively handle multi-hunk bugs requiring coordinated modifications across multiple code locations. To overcome this limitation, we propose MultiFixer, the first multi-agent framework featuring a coordinator–proposer architecture. MultiFixer integrates tool-augmented bug analysis, fine-grained contextual modeling, iterative patch generation, and a two-stage (syntactic–semantic) refinement mechanism to enable coordinated, cross-method and cross-file repairs with explicit repair sequencing. Evaluated on Defects4J, MultiFixer successfully fixes 326 bugs—including 62 multi-method and 27 multi-file defects—and achieves a new state of the art by repairing 420 bugs when combined with Claude-3.5-Sonnet, significantly outperforming current methods across multiple benchmark datasets.

Abstract

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.

Problem

• Automated Program Repair
• Multi-Hunk Bugs
• Large Language Models
• Repository-Level Context
• Patch Generation

Innovation

• Multi-Agent Framework
• Multi-Hunk Bug Repair
• Coordinator-Proposer Architecture
• Tool-Augmented Analysis
• Two-Stage Patch Refinement