{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:33:45Z","timestamp":1777102425500,"version":"3.51.4"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2023YFB2703600"],"award-info":[{"award-number":["2023YFB2703600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372492"],"award-info":[{"award-number":["62372492"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Laboratory of Data Space Technology and System","award":["QZQC2024016"],"award-info":[{"award-number":["QZQC2024016"]}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2023A1515010746, 2023A1515011474, 2023A1515012292"],"award-info":[{"award-number":["2023A1515010746, 2023A1515011474, 2023A1515012292"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>Commits messages play a crucial role in helping developers efficiently comprehend code modifications. Due to the time pressure of project iteration or poor message-writing practices, many commits suffer from missing messages. To address this issue, researchers have explored the automated generation of commit messages. Because of the truncation mechanism of the learning-based model, most of the current studies focus on code changes appearing at the beginning of a commit into the model for commit message generation. This may not be the best strategy for commit message generation because each code change in a commit contributes unequally to its overall purpose. To better generate commit messages, we propose a novel method that identifies the core code change in a commit for commit message generation. Specifically, we employ a method to predict the relative importance of the classes contained in a commit, and the code change of the class with the highest importance score (i.e., core change) is used to generate the commit message. Incorporating core change information can boost the performance of other existing methods (such as NMT, NNGen, and CoreGen). Building on this insight, we develop CCGen\u2014a Core Change-Based Generation model that integrates a Transformer architecture with CodeBERT-enhanced encoding to leverage code semantics. The experiment demonstrates that the proposed method for commit message generation outperforms the state-of-the-art by 18.47% on average across seven metrics including 19.97 on ROUGE-L.<\/jats:p>","DOI":"10.1145\/3750039","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T16:19:33Z","timestamp":1753287573000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Commit Messages Generation Based on Core Changes"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9548-0208","authenticated-orcid":false,"given":"Yuan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0349-0850","authenticated-orcid":false,"given":"Zhicao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8234-3186","authenticated-orcid":false,"given":"Xiangping","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion, School of Journalism and Communication, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1431-4073","authenticated-orcid":false,"given":"Changlin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7677-8766","authenticated-orcid":false,"given":"Xiaocong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3328833.3328873"},{"key":"e_1_3_2_3_2","unstructured":"Lei Jimmy Ba Jamie Ryan Kiros and Geoffrey E. Hinton. 2016. Layer normalization. arXiv:1607.06450. Retrieved from http:\/\/arxiv.org\/abs\/1607.06450"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201915)","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201915). Yoshua Bengio and Yann LeCun (Eds.), Retrieved from http:\/\/arxiv.org\/abs\/1409.0473"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/1858996.1859005"},{"issue":"4","key":"e_1_3_2_7_2","first-page":"1","article-title":"Xgboost: Extreme gradient boosting","volume":"1","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, and Kailong Chen. 2015. Xgboost: Extreme gradient boosting. R Package Version 0.4\u20132 1, 4 (2015), 1\u20134.","journal-title":"R Package Version 0.4\u20132"},{"key":"e_1_3_2_8_2","unstructured":"Junyoung Chung \u00c7aglar G\u00fcl\u00e7ehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. Retrieved from http:\/\/arxiv.org\/abs\/1412.3555"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/SCAM.2014.14"},{"key":"e_1_3_2_10_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2013.6624019"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00075"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510069"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606588"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3623306"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/1810295.1810335"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/WCRE.2010.13"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00369-8"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597926.3598096"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2772"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264841"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.3021902"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p16-1195"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2017.8115626"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPC.2017.12"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","unstructured":"Toshihiro Kamiya Shinji Kusumoto and Katsuro Inoue. 2002. CCFinder: A multilinguistic token-based code clone detection system for large scale source code. IEEE Transactions on Software Engineering 28 7 (2002) 654\u2013670. DOI: 10.1109\/TSE.2002.1019480","DOI":"10.1109\/TSE.2002.1019480"},{"key":"e_1_3_2_29_2","unstructured":"Yoon Kim Carl Denton Luong Hoang and Alexander M. Rush. 2017. Structured attention networks. arXiv:1702.00887. Retrieved from http:\/\/arxiv.org\/abs\/1702.00887"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2006.116"},{"key":"e_1_3_2_31_2","unstructured":"Oleksii Kuchaiev and Boris Ginsburg. 2017. Factorization tricks for LSTM networks. arXiv:1703.10722. Retrieved from http:\/\/arxiv.org\/abs\/1703.10722"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/0164-1212(93)90101-3"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134355"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2019.00056"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.02972"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238190"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00026"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2010.5463344"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.288"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2597008.2597793"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPC.2013.6613830"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.05.039"},{"key":"e_1_3_2_43_2","unstructured":"OpenAI. 2023. ChatGPT: Optimizing Language Models for Dialogue. Retrieved from https:\/\/openai.com\/blog\/chatgpt"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/1370750.1370765"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11219-010-9104-9"},{"key":"e_1_3_2_46_2","unstructured":"Nicolae-Teodor Pavel and Traian Rebedea. 2021. A sketch-based neural model for generating commit messages from diffs. arXiv:2104.04087. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2104.04087"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606676"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/2568225.2568247"},{"key":"e_1_3_2_49_2","unstructured":"Noam Shazeer Azalia Mirhoseini Krzysztof Maziarz Andy Davis Quoc V. Le Geoffrey E. Hinton and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv:1701.06538. Retrieved from http:\/\/arxiv.org\/abs\/1701.06538"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2016.162"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.372"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/1858996.1859006"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME52107.2021.00018"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-022-10219-1"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510205"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.229"},{"key":"e_1_3_2_57_2","first-page":"5998","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","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. In Proceedings of the Advances in Neural Information Processing Systems. Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.), Vol. 30, 5998\u20136008. Retrieved from http:\/\/papers.nips.cc\/paper\/7181-attention-is-all-you-need"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3155077.3155079"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/552"},{"key":"e_1_3_2_60_2","unstructured":"Xiangzhe Xu Zhuo Zhang Zian Su Ziyang Huang Shiwei Feng Yapeng Ye Nan Jiang Danning Xie Siyuan Cheng Lin Tan et al. 2024. Symbol preference aware generative models for recovering variable names from stripped binary. arXiv:2306.02546. Retrieved from https:\/\/arxiv.org\/abs\/2306.02546"},{"key":"e_1_3_2_61_2","unstructured":"Pengyu Xue Linhao Wu Zhongxing Yu Zhi Jin Zhen Yang Xinyi Li Zhenyu Yang and Yue Tan. 2024. Automated commit message generation with large language models: An empirical study and beyond. arXiv:2404.14824. Retrieved from https:\/\/arxiv.org\/abs\/2404.14824"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/2642937.2642994"},{"key":"e_1_3_2_63_2","unstructured":"Ting Zhang Ivana Clairine Irsan Ferdian Thung and David Lo .2024. CUPID: Leveraging ChatGPT for more accurate duplicate bug report detection. arXiv:2308.10022. Retrieved from https:\/\/arxiv.org\/abs\/2308.10022"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2024.3364675"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3750039","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T06:34:58Z","timestamp":1777098898000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3750039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":63,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3750039"],"URL":"https:\/\/doi.org\/10.1145\/3750039","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"2024-04-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}