{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:36:31Z","timestamp":1791459391397,"version":"4.3.4"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"OOPSLA2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402214,62232014"],"award-info":[{"award-number":["62402214,62232014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20241194"],"award-info":[{"award-number":["BK20241194"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025T180420"],"award-info":[{"award-number":["2025T180420"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZB20250386"],"award-info":[{"award-number":["GZB20250386"]}]},{"name":"Hong Kong Polytechnic University Fund","award":["P0051205,P0051074"],"award-info":[{"award-number":["P0051205,P0051074"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Program. Lang."],"published-print":{"date-parts":[[2025,10,9]]},"abstract":"<jats:p>Large-language models (LLMs) have been leveraged to enhance the capability of automated program repair techniques in recent research. While existing LLM-based program repair techniques compared favorably to other techniques based on heuristics, constraint-solving, and learning in producing high-quality patches, they mainly target bugs that can be corrected by changing a single faulty method, which greatly limits the effectiveness of such techniques in repairing bugs that demand patches spanning across multiple methods. In this work, we propose the PReMM technique to effectively propose patches changing multiple methods. PReMM builds on three core component techniques: the faulty method clustering technique to partition the faulty methods into clusters based on the dependence relationship among them, enabling a divide-and-conquer strategy for the repairing task; the fault context extraction technique to gather extra information about the fault context which can be utilized to better guide the diagnosis of the fault and the generation of correct patches; the dual-agent-based patch generation technique that employs two LLM-based agents with different roles to analyze the fault more precisely and generate patches of higher-quality. We have implemented the PReMM technique into a tool with the same name and applied the tool to repair real-world bugs from datasets Defects4J V1.2 and V2.0. PReMM produced correct patches for 307 bugs in total. Compared with ThinkRepair, the state-of-the-art LLM-based program repair technique, PReMM correctly repaired 102 more bugs, achieving an improvement of 49.8%.<\/jats:p>","DOI":"10.1145\/3763097","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T08:51:31Z","timestamp":1759999891000},"page":"1316-1344","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PReMM: LLM-Based Program Repair for Multi-method Bugs via Divide and Conquer"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4163-8994","authenticated-orcid":false,"given":"Linna","family":"Xie","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-3416","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6065-6958","authenticated-orcid":false,"given":"Yu","family":"Pei","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6737-9953","authenticated-orcid":false,"given":"Zhongzhen","family":"Wen","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-615X","authenticated-orcid":false,"given":"Kui","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0104-2731","authenticated-orcid":false,"given":"Tian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3090-9568","authenticated-orcid":false,"given":"Xuandong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2025. Interpret Odds Ratio - R Method. https:\/\/easystats.github.io\/effectsize\/reference\/interpret_oddsratio.html. Accessed: 2025-07."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.ijcnlp-main.45"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.bigscience-1.9"},{"key":"e_1_3_1_5_2","first-page":"25","volume-title":"In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS \u201920)","author":"Brown Tom B.","year":"2020","unstructured":"Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS \u201920). Curran Associates Inc., Red Hook, NY, USA, Article 159, 25 pages."},{"key":"e_1_3_1_6_2","unstructured":"chatgptendpoint. 2023. Introducing ChatGPT and Whisper APIs. https:\/\/openai.com\/blog\/introducing-chatgpt-and-whisper-apis."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610911003650383"},{"key":"e_1_3_1_8_2","unstructured":"Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Ponde de Oliveira Pinto Jared Kaplan Harri Edwards Yuri Burda Nicholas Joseph Greg Brockman Alex Ray Raul Puri Gretchen Krueger Michael Petrov Heidy Khlaaf Girish Sastry Pamela Mishkin Brooke Chan Scott Gray Nick Ryder Mikhail Pavlov Alethea Power Lukasz Kaiser Mohammad Bavarian Clemens Winter Philippe Tillet Felipe Petroski Such Dave Cummings Matthias Plappert Fotios Chantzis Elizabeth Barnes Ariel Herbert-Voss William Hebgen Guss Alex Nichol Alex Paino Nikolas Tezak Jie Tang Igor Babuschkin Suchir Balaji Shantanu Jain William Saunders Christopher Hesse Andrew N. Carr Jan Leike Josh Achiam Vedant Misra Evan Morikawa Alec Radford Matthew Knight Miles Brundage Mira Murati Katie Mayer Peter Welinder Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever and Wojciech Zaremba. 2021. Evaluating Large Language Models Trained on Code. arXiv:2107.03374 [cs.LG] https:\/\/arxiv.org\/abs\/2107.03374"},{"key":"e_1_3_1_9_2","first-page":"4302","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS\u201917)","author":"Brown Paul F","year":"2017","unstructured":"Paul F. Brown, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS\u201917). Curran Associates Inc., Red Hook, NY, USA, 4302\u20134310."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2593735.2593740"},{"key":"e_1_3_1_12_2","unstructured":"Dawn Drain Colin B. Clement Guillermo Serrato and Neel Sundaresan. 2023. DeepDebug: Fixing Python Bugs Using Stack Traces Backtranslation and Code Skeletons. arXiv:2105.09352 [cs.SE] https:\/\/arxiv.org\/abs\/2105.09352"},{"key":"e_1_3_1_13_2","unstructured":"Daniel Fried Armen Aghajanyan Jessy Lin Sida Wang Eric Wallace Freda Shi Ruiqi Zhong Wen tau Yih Luke Zettlemoyer and Mike Lewis. 2023. InCoder: A Generative Model for Code Infilling and Synthesis. arXiv:2204.05999 [cs.SE] https:\/\/arxiv.org\/abs\/2204.05999"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330559"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/506315.506316"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264600"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00111"},{"key":"e_1_3_1_18_2","first-page":"1161","volume-title":"Proceedings of the 43rd International Conference on Software Engineering (Madrid, Spain) (ICSE \u201921)","author":"Jiang Nan","year":"2023","unstructured":"Nan Jiang, Thibaud Lutellier, and Lin Tan. 2023. CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. In Proceedings of the 43rd International Conference on Software Engineering (Madrid, Spain) (ICSE \u201921). IEEE Press, 1161\u20131173. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00107"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2610384.2628055"},{"key":"e_1_3_1_20_2","unstructured":"Sophia D Kolak Ruben Martins Claire Le Goues and Vincent Josua Hellendoorn. 2022. Patch Generation with Language Models: Feasibility and Scaling Behavior. In Deep Learning for Code Workshop. https:\/\/openreview.net\/forum?"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106309"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2016.76"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2011.104"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2011.104"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","unstructured":"Fengjie Li Jiajun Jiang Jiajun Sun and Hongyu Zhang. 2023. Evaluating the Generalizability of LLMs in Automated Program Repair. In 2025 IEEE\/ACM 47th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). 91\u201395. https:\/\/doi.org\/10.1109\/ICSE-NIER66352.2025.00024 10.1109\/ICSE-NIER66352.2025.00024","DOI":"10.1109\/ICSE-NIER66352.2025.00024"},{"key":"e_1_3_1_26_2","unstructured":"R. Li L. B. Allal Y. Zi N. Muennighoff D. Kocetkov C. Mou M. Marone C. Akiki J. Li J. Chim Q. Liu E. Zheltonozhskii T. Y. Zhuo T. Wang O. Dehaene M. Davaadorj J. Lamy-Poirier J. Monteiro O. Shliazhko N. Gontier N. Meade A. Randy M-H. Yee L. K. Umapathi J. Zhu B. Lipkin M. Oblokulov Z. Wang R. Murthy J. Stillerman S. S. Patel D. Abulkhanov M. Zocca M. Dey Z. Zhang N. Fahmy U. Bhattacharyya S. Gunasekar W. Yu S. Singh S. Luccioni P. Villegas M. Kunakov F. Zhdanov M. Romero T. Lee N. Timor J. Ding C. Schlesinger H. Schoelkopf J. Ebert T. Dao M. Mishra A. Gu J. Robinson C. J. Anderson B. Dolan-Gavitt D. Contractor S. Reddy D. Fried D. Bahdanau Y. Jernite C. M. Ferrandis S. Hughes T. Wolf A. Guha L. von Werra and H. de Vries. [n.d.]. StarCoder: May the Source be With You! Transactions on machine learning research ([n. d.]). https:\/\/par.nsf.gov\/biblio\/10483982"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380345"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377812.3390896"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510177"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3680381"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","unstructured":"LinnaX7. 2025. LinnaX7\/PReMM: PReMM: LLM-Based Program Repair for Multi-Method Bugs via Divide and Conquer. https:\/\/doi.org\/10.5281\/zenodo.16927561 10.5281\/zenodo.16927561","DOI":"10.5281\/zenodo.16927561"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2019.8667970"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330577"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00638"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786811"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397369"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2931037.2948705"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02295996"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884807"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00127"},{"key":"e_1_3_1_42_2","unstructured":"OpenCompass. 2024. OpenCompass Rank. (2024). https:\/\/rank.opencompass.org.cn\/home."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2011"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3524459.3527351"},{"key":"e_1_3_1_45_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","unstructured":"Daniel Ramos Cl\u00e1udia Mamede Kush Jain Paulo Canelas Catarina Gamboa and Claire Le Goues. 2024. Are Large Language Models Memorizing Bug Benchmarks? CoRR abs\/2411.13323 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2411.13323 10.48550\/ARXIV.2411.13323 arXiv:2411.13323","DOI":"10.48550\/ARXIV.2411.13323"},{"key":"e_1_3_1_47_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2023. Proximal Policy Optimization Algorithms. arXiv:1707.06347 [cs.LG] https:\/\/arxiv.org\/abs\/1707.06347"},{"key":"e_1_3_1_48_2","unstructured":"John Schulman Barret Zoph Jacob Hilton Christina Kim Jacob Menick Jiayi Weng Juan Felipe Ceron Uribe Liam Fedus Luke Metz Michael Pokorny Rapha Gontijo Lopes Shengjia Zhao Arun Vijayvergiya Eric Sigler Adam Perelman Chelsea Voss Mike Heaton Joel Parish Dave Cummings Rajeev Nayak Valerie Balcom David Schnurr Tomer Kaftan Chris Hallacy Nicholas Turley Noah Deutsch Vik Goel Jonathan Ward Aris Konstantinidis Wojciech Zaremba Long Ouyang Leonard Bogdonoff Joshua Gross David Medina Sarah Yoo Teddy Lee Ryan Lowe Dan Mossing Joost Huizinga Roger Jiang Carroll Wainwright Diogo Almeida Steph Lin Marvin Zhang Kai Xiao Katarina Slama Steven Bills Alex Gray Jan Leike Jakub Pachocki Phil Tillet Shantanu Jain Greg Brockman and Nick Ryder. 2022. ChatGPT: Optimizing Language Models for Dialogue. (2022). https:\/\/openai.com\/blog\/chatgpt\/."},{"key":"e_1_3_1_49_2","doi-asserted-by":"crossref","unstructured":"Andr\u00e9 Silva Nuno Saavedra and Martin Monperrus. 2024. GitBug-Java: A Reproducible Benchmark of Recent Java Bugs. In 2024 IEEE\/ACM 21st International Conference on Mining Software Repositories (MSR). 118\u2013122.","DOI":"10.1145\/3643991.3644884"},{"key":"e_1_3_1_50_2","unstructured":"Qwen Team. 2024. Qwen2.5: A Party of Foundation Models. https:\/\/qwenlm.github.io\/blog\/qwen2.5\/"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3616271"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180233"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00129"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549101"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3680323"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556893"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556926"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510222"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3680359"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","unstructured":"Quanjun Zhang Chunrong Fang Tongke Zhang Bowen Yu Weisong Sun and Zhenyu Chen. 2023. Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 535\u2013547. https:\/\/doi.org\/10.1109\/ASE56229.2023.00063 10.1109\/ASE56229.2023.00063","DOI":"10.1109\/ASE56229.2023.00063"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468544"},{"key":"e_1_3_1_63_2","unstructured":"Daniel M. Brown Nisan Stiennon Jeffrey Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano and Geoffrey Irving. 2020. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593 [cs.CL] https:\/\/arxiv.org\/abs\/1909.08593"}],"container-title":["Proceedings of the ACM on Programming Languages"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3763097","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:13:15Z","timestamp":1784196795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3763097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":62,"journal-issue":{"issue":"OOPSLA2","published-print":{"date-parts":[[2025,10,9]]}},"alternative-id":["10.1145\/3763097"],"URL":"https:\/\/doi.org\/10.1145\/3763097","relation":{},"ISSN":["2475-1421"],"issn-type":[{"value":"2475-1421","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,9]]},"assertion":[{"value":"2025-03-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}