{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T20:24:46Z","timestamp":1793478286840,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":86,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["CCF-2131943"],"award-info":[{"award-number":["CCF-2131943"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,11]]},"DOI":"10.1145\/3650212.3680323","type":"proceedings-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:44:25Z","timestamp":1726040665000},"page":"819-831","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":220,"title":["Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8660-9324","authenticated-orcid":false,"given":"Chunqiu Steven","family":"Xia","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5175-2702","authenticated-orcid":false,"given":"Lingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Yejin Bang Samuel Cahyawijaya Nayeon Lee Wenliang Dai Dan Su Bryan Wilie Holy Lovenia Ziwei Ji Tiezheng Yu and Willy Chung. 2023. A multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity. arXiv preprint arXiv:2302.04023.","DOI":"10.18653\/v1\/2023.ijcnlp-main.45"},{"key":"e_1_3_2_1_2_1","volume-title":"The Plastic Surgery Hypothesis. In FSE","author":"Barr Earl T.","year":"2014","unstructured":"Earl T. Barr, Yuriy Brun, Premkumar Devanbu, Mark Harman, and Federica Sarro. 2014. The Plastic Surgery Hypothesis. In FSE 2014. 306\u2013317."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Samuel Benton Xia Li Yiling Lou and Lingming Zhang. 2020. On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair Systems. In ASE. 907\u2013918.","DOI":"10.1145\/3324884.3416566"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the ACL Workshop on Challenges & Perspectives in Creating Large Language Models. arXiv:2204","author":"Black Sid","year":"2022","unstructured":"Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022. GPT-NeoX-20B: An Open-Source Autoregressive Language Model. In Proceedings of the ACL Workshop on Challenges & Perspectives in Creating Large Language Models. arXiv:2204.06745"},{"key":"e_1_3_2_1_5_1","volume-title":"Repairagent: An autonomous, llm-based agent for program repair. arXiv preprint arXiv:2403.17134.","author":"Bouzenia Islem","year":"2024","unstructured":"Islem Bouzenia, Premkumar Devanbu, and Michael Pradel. 2024. Repairagent: An autonomous, llm-based agent for program repair. arXiv preprint arXiv:2403.17134."},{"key":"e_1_3_2_1_6_1","volume-title":"Language models are few-shot learners. Advances in neural information processing systems, 33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, and Amanda Askell. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33 (2020), 1877\u20131901."},{"key":"e_1_3_2_1_7_1","unstructured":"2023. Introducing ChatGPT and Whisper APIs. https:\/\/openai.com\/blog\/introducing-chatgpt-and-whisper-apis"},{"key":"e_1_3_2_1_8_1","unstructured":"2023. ChatGPT Guide. https:\/\/platform.openai.com\/docs\/guides\/chat"},{"key":"e_1_3_2_1_9_1","unstructured":"2023. ChatGPT Web. https:\/\/chat.openai.com\/chat"},{"key":"e_1_3_2_1_10_1","unstructured":"Dong Chen Shaoxin Lin Muhan Zeng Daoguang Zan Jian-Gang Wang Anton Cheshkov Jun Sun Hao Yu Guoliang Dong and Artem Aliev. 2024. CodeR: Issue Resolving with Multi-Agent and Task Graphs. arXiv preprint arXiv:2406.01304."},{"key":"e_1_3_2_1_11_1","volume-title":"2017 32nd IEEE\/ACM International Conference on Automated Software Engineering (ASE). 637\u2013647","author":"Chen Liushan","unstructured":"Liushan Chen, Yu Pei, and Carlo A. Furia. 2017. Contract-based program repair without the contracts. In 2017 32nd IEEE\/ACM International Conference on Automated Software Engineering (ASE). 637\u2013647."},{"key":"e_1_3_2_1_12_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, and Greg Brockman.","author":"Chen Mark","year":"2021","unstructured":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, and Greg Brockman. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374."},{"key":"e_1_3_2_1_13_1","unstructured":"Xinyun Chen Maxwell Lin Nathanael Sch\u00e4rli and Denny Zhou. 2023. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128."},{"key":"e_1_3_2_1_14_1","article-title":"SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair","author":"Chen Zimin","year":"2019","unstructured":"Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-No\u00ebl Pouchet, Denys Poshyvanyk, and Martin Monperrus. 2019. SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair. IEEE Transaction on Software Engineering.","journal-title":"IEEE Transaction on Software Engineering."},{"key":"e_1_3_2_1_15_1","unstructured":"2023. Dataset. https:\/\/figshare.com\/s\/9796028cef4d7dbc08ff"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2593735.2593740"},{"key":"e_1_3_2_1_17_1","volume-title":"46th International Conference on Software Engineering (ICSE).","author":"Deng Yinlin","year":"2024","unstructured":"Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang. 2024. Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT. In 46th International Conference on Software Engineering (ICSE)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2896921.2896931"},{"key":"e_1_3_2_1_19_1","unstructured":"Hasan Ferit Eniser Hanliang Zhang Cristina David Meng Wang Brandon Paulsen Joey Dodds and Daniel Kroening. 2024. Towards Translating Real-World Code with LLMs: A Study of Translating to Rust. arXiv preprint arXiv:2405.11514."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Zhangyin Feng Daya Guo Duyu Tang Nan Duan Xiaocheng Feng Ming Gong Linjun Shou Bing Qin Ting Liu Daxin Jiang and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. arXiv:2002.08155","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_21_1","unstructured":"Daniel Fried Armen Aghajanyan Jessy Lin Sida Wang Eric Wallace Freda Shi Ruiqi Zhong Wen-tau Yih Luke Zettlemoyer and Mike Lewis. 2022. InCoder: A Generative Model for Code Infilling and Synthesis. arXiv:2204.05999"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2755013"},{"key":"e_1_3_2_1_23_1","first-page":"6224","volume-title":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA","author":"Ghanbari Ali","year":"2019","unstructured":"Ali Ghanbari, Samuel Benton, and Lingming Zhang. 2019. Practical Program Repair via Bytecode Mutation. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019). ACM, 19\u201330. isbn:978-1-4503-6224-5"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318162"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2337223.2337322"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264600"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213871"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00125"},{"key":"e_1_3_2_1_29_1","volume-title":"CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE), May.","author":"Jiang Nan","year":"2021","unstructured":"Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021. CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE), May."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00069"},{"key":"e_1_3_2_1_31_1","volume-title":"ISSTA","author":"Just Ren\u00e9","year":"2014","unstructured":"Ren\u00e9 Just, Darioush Jalali, and Michael D. Ernst. 2014. Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs. ISSTA 2014. Association for Computing Machinery, New York, NY, USA. 437\u2013440. isbn:9781450326452"},{"key":"e_1_3_2_1_32_1","volume-title":"Patch Generation with Language Models: Feasibility and Scaling Behavior. In Deep Learning for Code Workshop.","author":"Kolak Sophia D","year":"2022","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."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09780-z"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106309"},{"key":"e_1_3_2_1_35_1","volume-title":"History Driven Program Repair. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). 1, 213\u2013224","author":"Le Xuan Bach D.","year":"2016","unstructured":"Xuan Bach D. Le, David Lo, and Claire Le Goues. 2016. History Driven Program Repair. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). 1, 213\u2013224."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2012.6227211"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2011.104"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering (ICSE \u201920)","author":"Li Yi","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. DLFix: Context-Based Code Transformation Learning for Automated Program Repair. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering (ICSE \u201920). Association for Computing Machinery, New York, NY, USA. 602\u2013614. isbn:9781450371216"},{"key":"e_1_3_2_1_39_1","volume-title":"QuixBugs: A Multi-Lingual Program Repair Benchmark Set Based on the Quixey Challenge. SPLASH Companion","author":"Lin Derrick","year":"2017","unstructured":"Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017. QuixBugs: A Multi-Lingual Program Repair Benchmark Set Based on the Quixey Challenge. SPLASH Companion 2017. Association for Computing Machinery, New York, NY, USA. 55\u201356. isbn:9781450355148"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330577"},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering. 456\u2013467","author":"Liu Kui","unstructured":"Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawend\u00e9 F. Bissyand\u00e9. 2019. AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations. In Proceedings of the 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering. 456\u2013467."},{"key":"e_1_3_2_1_42_1","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, 55, 9 (2023), 1\u201335.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786811"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884872"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2020","author":"Lou Yiling","year":"2020","unstructured":"Yiling Lou, Ali Ghanbari, Xia Li, Lingming Zhang, Haotian Zhang, Dan Hao, and Lu Zhang. 2020. Can automatd program repair refine fault localization? A unified debugging approach. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2020, Los Angeles, California, United States, July 18-22 , 2020.. 12 pages."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397369"},{"key":"e_1_3_2_1_47_1","unstructured":"Yingwei Ma Qingping Yang Rongyu Cao Binhua Li Fei Huang and Yongbin Li. 2024. How to Understand Whole Software Repository? arXiv preprint arXiv:2406.01422."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Matias Martinez Thomas Durieux Jifeng Xuan Romain Sommerard and Martin Monperrus. 2015. Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset. arXiv:1505.07002","DOI":"10.1007\/s10664-016-9470-4"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2931037.2948705"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884807"},{"key":"e_1_3_2_1_51_1","volume-title":"Template-based Neural Program Repair. In ICSE","author":"Meng Xiangxin","year":"2023","unstructured":"Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu, and Chunming Hu. 2023. Template-based Neural Program Repair. In ICSE 2023. 1456\u20131468."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606623"},{"key":"e_1_3_2_1_53_1","volume-title":"Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474.","author":"Nijkamp Erik","year":"2022","unstructured":"Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474."},{"key":"e_1_3_2_1_54_1","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, and Alex Ray. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35 (2022), 27730\u201327744.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering. 1\u201313","author":"Pan Rangeet","year":"2024","unstructured":"Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, Divya Sankar, Lambert Pouguem Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand. 2024. Lost in translation: A study of bugs introduced by large language models while translating code. In Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering. 1\u201313."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3524459.3527351"},{"key":"e_1_3_2_1_57_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884848"},{"key":"e_1_3_2_1_59_1","article-title":"An empirical evaluation of using large language models for automated unit test generation","author":"Sch\u00e4fer Max","year":"2023","unstructured":"Max Sch\u00e4fer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023. An empirical evaluation of using large language models for automated unit test generation. IEEE Transactions on Software Engineering.","journal-title":"IEEE Transactions on Software Engineering."},{"key":"e_1_3_2_1_60_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv:1707.06347"},{"key":"e_1_3_2_1_61_1","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. https:\/\/openai.com\/blog\/chatgpt\/"},{"key":"e_1_3_2_1_62_1","unstructured":"Jessica Shieh. 2023. Best practices for prompt engineering with OpenAI API. https:\/\/help.openai.com\/en\/articles\/6654000-best-practices-for-prompt-engineering-with-openai-api"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786825"},{"key":"e_1_3_2_1_64_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. arXiv:1409.3215"},{"key":"e_1_3_2_1_65_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention Is All You Need. arXiv:1706.03762"},{"key":"e_1_3_2_1_66_1","unstructured":"Xingyao Wang Boxuan Li Yufan Song Frank F Xu Xiangru Tang Mingchen Zhuge Jiayi Pan Yueqi Song Bowen Li and Jaskirat Singh. 2024. OpenDevin: An Open Platform for AI Software Developers as Generalist Agents. arXiv preprint arXiv:2407.16741."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180233"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2521368"},{"key":"e_1_3_2_1_69_1","volume-title":"Condefects: A new dataset to address the data leakage concern for llm-based fault localization and program repair. arXiv preprint arXiv:2310.16253.","author":"Wu Yonghao","year":"2023","unstructured":"Yonghao Wu, Zheng Li, Jie M Zhang, and Yong Liu. 2023. Condefects: A new dataset to address the data leakage concern for llm-based fault localization and program repair. arXiv preprint arXiv:2310.16253."},{"key":"e_1_3_2_1_70_1","volume-title":"The Plastic Surgery Hypothesis in the Era of Large Language Models. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE).","author":"Xia Chunqiu Steven","year":"2023","unstructured":"Chunqiu Steven Xia, Yifeng Ding, and Lingming Zhang. 2023. The Plastic Surgery Hypothesis in the Era of Large Language Models. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00129"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549101"},{"key":"e_1_3_2_1_73_1","volume-title":"Deep Learning for Code Workshop.","author":"Xia Chunqiu Steven","year":"2023","unstructured":"Chunqiu Steven Xia and Lingming Zhang. 2023. Conversational automated program repair. In Deep Learning for Code Workshop."},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3520312.3534862"},{"key":"e_1_3_2_1_75_1","volume-title":"Kernelgpt: Enhanced kernel fuzzing via large language models. arXiv preprint arXiv:2401.00563.","author":"Yang Chenyuan","year":"2023","unstructured":"Chenyuan Yang, Zijie Zhao, and Lingming Zhang. 2023. Kernelgpt: Enhanced kernel fuzzing via large language models. arXiv preprint arXiv:2401.00563."},{"key":"e_1_3_2_1_76_1","volume-title":"Swe-agent: Agent-computer interfaces enable automated software engineering. arXiv preprint arXiv:2405.15793.","author":"Yang John","year":"2024","unstructured":"John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024. Swe-agent: Agent-computer interfaces enable automated software engineering. arXiv preprint arXiv:2405.15793."},{"key":"e_1_3_2_1_77_1","volume-title":"SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics. In 37th IEEE\/ACM International Conference on Automated Software Engineering (ASE22)","author":"Ye He","year":"2022","unstructured":"He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, and Martin Monperrus. 2022. SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics. In 37th IEEE\/ACM International Conference on Automated Software Engineering (ASE22). Association for Computing Machinery, Article 92, 13 pages."},{"key":"e_1_3_2_1_78_1","volume-title":"Neural Program Repair with Execution-based Backpropagation. In 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE). 1506\u20131518","author":"Ye He","year":"2022","unstructured":"He Ye, Matias Martinez, and Martin Monperrus. 2022. Neural Program Repair with Execution-based Backpropagation. In 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE). 1506\u20131518."},{"key":"e_1_3_2_1_79_1","unstructured":"Junjie Ye Xuanting Chen Nuo Xu Can Zu Zekai Shao Shichun Liu Yuhan Cui Zeyang Zhou Chao Gong and Yang Shen. 2023. A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models. arXiv preprint arXiv:2303.10420."},{"key":"e_1_3_2_1_80_1","unstructured":"Zhiqiang Yuan Yiling Lou Mingwei Liu Shiji Ding Kaixin Wang Yixuan Chen and Xin Peng. 2023. No more manual tests? evaluating and improving chatgpt for unit test generation. arXiv preprint arXiv:2305.04207."},{"key":"e_1_3_2_1_81_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021.","author":"Yue Wang Shafiq Joty","unstructured":"Shafiq Joty Yue Wang, Weishi Wang and Steven C.H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"crossref","unstructured":"Yuntong Zhang Haifeng Ruan Zhiyu Fan and Abhik Roychoudhury. 2024. AutoCodeRover: Autonomous Program Improvement. arxiv:2404.05427.","DOI":"10.1145\/3650212.3680384"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468544"},{"key":"e_1_3_2_1_84_1","volume-title":"Tare: Type-Aware Neural Program Repair. In ICSE","author":"Zhu Qihao","year":"2023","unstructured":"Qihao Zhu, Zeyu Sun, Wenjie Zhang, Yingfei Xiong, and Lu Zhang. 2023. Tare: Type-Aware Neural Program Repair. In ICSE 2023."},{"key":"e_1_3_2_1_85_1","unstructured":"Daniel M. Ziegler Nisan Stiennon Jeffrey Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano and Geoffrey Irving. 2019. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2892102"}],"event":{"name":"ISSTA '24: 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis","location":"Vienna Austria","acronym":"ISSTA '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","AITO"]},"container-title":["Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3680323","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650212.3680323","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:50:07Z","timestamp":1750272607000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3680323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":86,"alternative-id":["10.1145\/3650212.3680323","10.1145\/3650212"],"URL":"https:\/\/doi.org\/10.1145\/3650212.3680323","relation":{},"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}