{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:22:16Z","timestamp":1779880936781,"version":"3.53.1"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,6]]},"DOI":"10.1145\/3597503.3623306","type":"proceedings-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T20:53:16Z","timestamp":1707252796000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":108,"title":["Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8002-8068","authenticated-orcid":false,"given":"Qi","family":"Guo","sequence":"first","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4118-7376","authenticated-orcid":false,"given":"Junming","family":"Cao","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-6502","authenticated-orcid":false,"given":"Xiaofei","family":"Xie","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5598-4006","authenticated-orcid":false,"given":"Shangqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0752-6764","authenticated-orcid":false,"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7238-7492","authenticated-orcid":false,"given":"Bihuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3376-2581","authenticated-orcid":false,"given":"Xin","family":"Peng","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Fatih Kadir Akin. 2023. Awesome Chatgpt Prompts. https:\/\/github.com\/f\/awesome-chatgpt-prompts."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP52600.2021.00022"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2013.23"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2015.21"},{"key":"e_1_3_2_1_5_1","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. arXiv:2005.14165 [cs.CL]"},{"key":"e_1_3_2_1_6_1","unstructured":"ChatGPTCodeReview. 2023. ExtraResourceLinkage. https:\/\/sites.google.com\/view\/chatgptcodereview."},{"key":"e_1_3_2_1_7_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al.","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, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)."},{"key":"e_1_3_2_1_8_1","unstructured":"Guo et al. 2023. chatgptcodereview-settings. https:\/\/sites.google.com\/view\/chatgptcodereview\/impact-of-settings?authuser=0."},{"key":"e_1_3_2_1_9_1","volume-title":"Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Daya Guo Shuai Lu Nan Duan Yanlin Wang Ming Zhou and Jian Yin. 2022. UniXcoder: Unified Cross-Modal Pre-training for Code Representation. arXiv:2203.03850","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"e_1_3_2_1_11_1","volume-title":"Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366","author":"Guo Daya","year":"2020","unstructured":"Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3473134"},{"key":"e_1_3_2_1_13_1","volume-title":"IBM Global AI Adoption Index","author":"IBM.","year":"2022","unstructured":"IBM. 2022. IBM Global AI Adoption Index 2022. https:\/\/www.ibm.com\/watson\/resources\/ai-adoption."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00107"},{"key":"e_1_3_2_1_15_1","unstructured":"Xue Jiang Zhuoran Zheng Chen Lyu Liang Li and Lei Lyu. 2021. TreeBERT: A tree-based pre-trained model for programming language. In Uncertainty in Artificial Intelligence. PMLR 54--63."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00026"},{"key":"e_1_3_2_1_17_1","volume-title":"Multi-target Backdoor Attacks for Code Pre-trained Models. arXiv preprint arXiv:2306.08350","author":"Li Yanzhou","year":"2023","unstructured":"Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, and Yang Liu. 2023. Multi-target Backdoor Attacks for Code Pre-trained Models. arXiv preprint arXiv:2306.08350 (2023)."},{"key":"e_1_3_2_1_18_1","volume-title":"CodeReviewer: Pre-Training for Automating Code Review Activities. arXiv preprint arXiv:2203.09095v1","author":"Li Zhiyu","year":"2022","unstructured":"Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, and Neel Sundaresan. 2022. CodeReviewer: Pre-Training for Automating Code Review Activities. arXiv preprint arXiv:2203.09095v1 (2022)."},{"key":"e_1_3_2_1_19_1","unstructured":"Shangqing Liu Yanzhou Li Xiaofei Xie and Yang Liu. 2023. CommitBART: A Large Pre-trained Model for GitHub Commits. arXiv:2208.08100"},{"key":"e_1_3_2_1_20_1","volume-title":"ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning. arXiv preprint arXiv:2301.09072","author":"Liu Shangqing","year":"2023","unstructured":"Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, and Yang Liu. 2023. ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning. arXiv preprint arXiv:2301.09072 (2023)."},{"key":"e_1_3_2_1_21_1","volume-title":"Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664","author":"Lu Shuai","year":"2021","unstructured":"Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al. 2021. Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664 (2021)."},{"key":"e_1_3_2_1_22_1","volume-title":"The Scope of ChatGPT in Software Engineering: A Thorough Investigation. arXiv preprint arXiv:2305.12138","author":"Ma Wei","year":"2023","unstructured":"Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023. The Scope of ChatGPT in Software Engineering: A Thorough Investigation. arXiv preprint arXiv:2305.12138 (2023)."},{"key":"e_1_3_2_1_23_1","volume-title":"Is Self-Attention Powerful to Learn Code Syntax and Semantics? arXiv preprint arXiv:2212.10017","author":"Ma Wei","year":"2022","unstructured":"Wei Ma, Mengjie Zhao, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jie Zhang, Wenhan Wang, and Yang Liu. 2022. Is Self-Attention Powerful to Learn Code Syntax and Semantics? arXiv preprint arXiv:2212.10017 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00041"},{"key":"e_1_3_2_1_25_1","volume-title":"Interrater reliability: the kappa statistic. Biochemia medica 22, 3","author":"McHugh Mary L","year":"2012","unstructured":"Mary L McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia medica 22, 3 (2012), 276--282."},{"key":"e_1_3_2_1_26_1","unstructured":"OpenAI. 2023. ChatGPTblog. https:\/\/openai.com\/blog\/chatgpt."},{"key":"e_1_3_2_1_27_1","unstructured":"OpenAI. 2023. gpt-3.5-turbo. https:\/\/platform.openai.com\/docs\/models\/gpt-3-5."},{"key":"e_1_3_2_1_29_1","unstructured":"Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray John Schulman Jacob Hilton Fraser Kelton Luke Miller Maddie Simens Amanda Askell Peter Welinder Paul Christiano Jan Leike and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. arXiv:2203.02155"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387475"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"e_1_3_2_1_32_1","volume-title":"Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476","author":"Qin Chengwei","year":"2023","unstructured":"Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476 (2023)."},{"key":"e_1_3_2_1_33_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_1_34_1","volume-title":"Yanqi Zhou, Wei Li, and Peter J. Liu.","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, NoamShazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michatgpt promptsel Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:1910.10683"},{"key":"e_1_3_2_1_35_1","unstructured":"Scipy. 2023. ttest_ind. https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.stats.ttest_ind.html."},{"key":"e_1_3_2_1_36_1","first-page":"3008","article-title":"Learning to summarize with human feedback","volume":"33","author":"Stiennon Nisan","year":"2020","unstructured":"Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learning to summarize with human feedback. Advances in Neural Information Processing Systems 33 (2020), 3008--3021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417058"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510067"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2015.7081824"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00021"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510621"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00027"},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696--8708","author":"Wang Yue","unstructured":"Yue Wang, Weishi Wang, Shafiq Joty, 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. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696--8708."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485275"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2015.2500238"},{"key":"e_1_3_2_1_46_1","volume-title":"Generation-based Code Review Automation: How Far Are We? arXiv preprint arXiv:2303.07221","author":"Zhou Xin","year":"2023","unstructured":"Xin Zhou, Kisub Kim, Bowen Xu, DongGyun Han, Junda He, and David Lo. 2023. Generation-based Code Review Automation: How Far Are We? arXiv preprint arXiv:2303.07221 (2023)."}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3623306","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3623306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:45Z","timestamp":1750182525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3623306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":45,"alternative-id":["10.1145\/3597503.3623306","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3623306","relation":{},"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"2024-02-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}