MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation

TL;DR

MultiMend introduces a multilingual, context-augmented, multi-hunk patch generation approach for automated program repair, improving effectiveness across multiple programming languages and bug types.

Contribution

It presents a novel language-independent method combining context augmentation and multi-hunk patch generation to enhance automated program repair performance.

Findings

Fixed 2,227 bugs across six benchmarks.

Achieved 1,545 exact developer patch matches.

Successfully repaired 121 multi-hunk bugs.

Abstract

Debugging software remains a labor-intensive and time-consuming process despite advances in testing and verification. Learning-based automated program repair (APR) has shown promise in reducing the effort of manually fixing bugs. However, existing techniques face several challenges, including language-dependent strategies, limited bug context utilization, and difficulties in handling bugs that span multiple locations in the code. This paper presents MultiMend, a multilingual learning-based APR approach designed to improve repair performance through language-independent context augmentation and multi-hunk patch generation. MultiMend fine-tunes a pre-trained code language model to generate bug-fixing patches. It embeds source code lines and applies retrieval-augmented generation to augment the usual function-based buggy context with relevant lines during patch generation. The approach also systematically constructs patches for multi-hunk bugs to extend the capabilities of single-hunk models and reduce the needed patch validations. We evaluate MultiMend on six benchmarks with 5,501 bugs covering four programming languages and compare it with state-of-the-art methods. Results show that MultiMend achieves competitive effectiveness and efficiency, fixing 2,227 bugs, of which 1,545 are identical to the developer's patch, and 121 are for multi-hunk bugs. Both context augmentation and multi-hunk patch generation contribute positively to these results. Overall, MultiMend's contributions are promising and offer practical and effective techniques to enhance APR performance for real-world software maintenance.