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

[2501.16044] MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2501.16044 (cs) [Submitted on 27 Jan 2025 ( v1 ), last revised 27 Dec 2025 (this version, v2)] Title: MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation Authors: Reza Gharibi , Mohammad Hadi Sadreddini , Seyed Mostafa Fakhrahmad View a PDF of the paper titled MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation, by Reza Gharibi and 2 other authors View PDF HTML (experimental) 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. Subjects: Software Engineering (cs.SE) Cite as: arXiv:2501.16044 [cs.SE] (or arXiv:2501.16044v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2501.16044 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Autom Softw Eng 33, 69 (2026) Related DOI : https://doi.org/10.1007/s10515-026-00611-2 Focus to learn more DOI(s) linking to related resources Submission history From: Reza Gharibi [ view email ] [v1] Mon, 27 Jan 2025 13:37:43 UTC (565 KB) [v2] Sat, 27 Dec 2025 19:04:46 UTC (327 KB) Full-text links: Access Paper: View a PDF of the paper titled MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation, by Reza Gharibi and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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