Fetching the paper…
Reading the bibliography…
Commit message generation (CMG) is a challenging task in automated software engineering that aims to generate natural language descriptions of code changes for commits.
A. Zagalsky, J. Feliciano, M.-A. Storey, Y. Zhao, and W. Wang, “The emergence of github as a collaborative platform for education,” in Proceedings of the 18th ACM conference on computer supported cooperative work & social computing , 2015, pp. 1906–1917
1917
Earlier work this paper cites.
J. L. Fleiss and J. Cohen, “The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability,” Educational and psychological measurement , vol. 33, no. 3, pp. 613–619, 1973
1973
Earlier work this paper cites.
F. Murtagh, “Multilayer perceptrons for classification and regression,” Neurocomputing , vol. 2, no. 5-6, pp. 183–197, 1991
1991
Earlier work this paper cites.
M. Paterson and V. Dančík, “Longest common subsequences,” in Mathematical Foundations of Computer Science 1994: 19th International Symposium, MFCS’94 Košice, Slovakia, August 22–26, 1994 Proceedings 19 . Springer, 1994, pp. 127–142
1994
Earlier work this paper cites.
Mockus and Votta, “Identifying reasons for software changes using historic databases,” in Proceedings 2000 International Conference on Software Maintenance , 2000, pp. 120–130
2000
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72
2005
Earlier work this paper cites.
J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?” in Proceedings of the 28th International Conference on Software Engineering , ser. ICSE ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 361–370. [Online]. Available: https://doi.org/10.1145/1134285.1134336
2006
Earlier work this paper cites.
N. Dragan, M. L. Collard, and J. I. Maletic, “Reverse engineering method stereotypes,” in 2006 22nd IEEE International Conference on Software Maintenance . IEEE, 2006, pp. 24–34
2006
Earlier work this paper cites.
N. Bettenburg, S. Just, A. Schröter, C. Weiss, R. Premraj, and T. Zimmermann, “What makes a good bug report?” in Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering , 2008, pp. 308–318
2008
Earlier work this paper cites.
J. Aranda and G. Venolia, “The secret life of bugs: Going past the errors and omissions in software repositories,” in 2009 IEEE 31st international conference on software engineering . IEEE, 2009, pp. 298–308
2009
Earlier work this paper cites.
S. Breu, R. Premraj, J. Sillito, and T. Zimmermann, “Frequently asked questions in bug reports,” University of Calgary, Tech. Rep., 2009
2009
Earlier work this paper cites.
R. P. Buse and W. R. Weimer, “Automatically documenting program changes,” in Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’10. New York, NY, USA: Association for Computing Machinery, 2010, p. 33–42. [Online]. Available: https://doi.org/10.1145/1858996.1859005
2010
Earlier work this paper cites.
W. Maalej and H.-J. Happel, “Can development work describe itself?” in 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) , 2010, pp. 191–200
2010
Earlier work this paper cites.
R. P. Buse and W. R. Weimer, “Automatically documenting program changes,” in Proceedings of the IEEE/ACM international conference on Automated software engineering , 2010, pp. 33–42
2010
Earlier work this paper cites.
P. E. McKnight and J. Najab, “Mann-whitney u test,” The Corsini encyclopedia of psychology , pp. 1–1, 2010
2010
Earlier work this paper cites.
N. Dragan, M. L. Collard, M. Hammad, and J. I. Maletic, “Using stereotypes to help characterize commits,” in 2011 27th IEEE International Conference on Software Maintenance (ICSM) . IEEE, 2011, pp. 520–523
2011
Earlier work this paper cites.
R. Dyer, H. A. Nguyen, H. Rajan, and T. N. Nguyen, “Boa: A language and infrastructure for analyzing ultra-large-scale software repositories,” in 2013 35th International Conference on Software Engineering (ICSE) , 2013, pp. 422–431
2013
Earlier work this paper cites.
O. Baysal, R. Holmes, and M. W. Godfrey, “Situational awareness: personalizing issue tracking systems,” in 2013 35th International Conference on Software Engineering (ICSE) . IEEE, 2013, pp. 1185–1188
2013
Earlier work this paper cites.
S. Onoue, H. Hata, and K.-i. Matsumoto, “A study of the characteristics of developers’ activities in github,” in 2013 20th Asia-Pacific Software Engineering Conference (APSEC) , vol. 2, 2013, pp. 7–12
2013
Earlier work this paper cites.
L. F. Cortés-Coy, M. Linares-Vásquez, J. Aponte, and D. Poshyvanyk, “On automatically generating commit messages via summarization of source code changes,” in 2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation , 2014, pp. 275–284
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
T.-D. B. Le, M. Linares-Vásquez, D. Lo, and D. Poshyvanyk, “Rclinker: Automated linking of issue reports and commits leveraging rich contextual information,” in 2015 IEEE 23rd International Conference on Program Comprehension . IEEE, 2015, pp. 36–47
2015
Earlier work this paper cites.
R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “Cider: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4566–4575
2015
Cited alongside, same era.
T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Lisbon, Portugal: Association for Computational Linguistics, Sep. 2015, pp. 1412–1421. [Online]. Available: https://aclanthology.org/D15-1166
2015
Cited alongside, same era.
J. Shen, X. Sun, B. Li, H. Yang, and J. Hu, “On automatic summarization of what and why information in source code changes,” in 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) , vol. 1. IEEE, 2016, pp. 103–112
2016
Cited alongside, same era.
H. Wang, X. Xia, D. Lo, Q. He, X. Wang, and J. Grundy, “Context-aware retrieval-based deep commit message generation,” ACM Trans. Softw. Eng. Methodol. , vol. 30, no. 4, jul 2021. [Online]. Available: https://doi.org/10.1145/3464689
2021
Later among the works it cites.
J. Li, T. Tang, W. X. Zhao, and J.-R. Wen, “Pretrained language model for text generation: A survey,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 , Z.-H. Zhou, Ed. International Joint Conferences on Artificial Intelligence Organization, 8 2021, pp. 4492–4499, survey Track. [Online]. Available: https://doi.org/10.24963/ijcai.2021/612
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Zhu, M. Zhou, and A. Mockus, “Effectiveness of code contribution: From patch-based to pull-request-based tools,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering , ser. FSE 2016. New York, NY, USA: Association for Computing Machinery, 2016, p. 871–882. [Online]. Available: https://doi.org/10.1145/2950290.2950364
2016
Cited alongside, same era.
G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, “Neural architectures for named entity recognition,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . San Diego, California: Association for Computational Linguistics, Jun. 2016, pp. 260–270. [Online]. Available: https://aclanthology.org/N16-1030
2016
Cited alongside, same era.
S. Jiang, A. Armaly, and C. McMillan, “Automatically generating commit messages from diffs using neural machine translation,” in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2017, pp. 135–146
2017
Cited alongside, same era.
Y. Zhou and A. Sharma, “Automated identification of security issues from commit messages and bug reports,” in Proceedings of the 2017 11th joint meeting on foundations of software engineering , 2017, pp. 914–919
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Cited alongside, same era.
Z. Liu, X. Xia, A. E. Hassan, D. Lo, Z. Xing, and X. Wang, “Neural-machine-translation-based commit message generation: How far are we?” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , ser. ASE ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 373–384. [Online]. Available: https://doi.org/10.1145/3238147.3238190
2018
Cited alongside, same era.
A. A. Sawant, G. Huang, G. Vilen, S. Stojkovski, and A. Bacchelli, “Why are features deprecated? an investigation into the motivation behind deprecation,” in 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) , 2018, pp. 13–24
2018
Cited alongside, same era.
G. Destefanis, M. Ortu, D. Bowes, M. Marchesi, and R. Tonelli, “On measuring affects of github issues’ commenters,” in Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering , ser. SEmotion ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 14–19. [Online]. Available: https://doi.org/10.1145/3194932.3194936
2018
Cited alongside, same era.
Z. Liao, D. He, Z. Chen, X. Fan, Y. Zhang, and S. Liu, “Exploring the characteristics of issue-related behaviors in github using visualization techniques,” IEEE Access , vol. 6, pp. 24 003–24 015, 2018
2018
Cited alongside, same era.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “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 . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 8696–8708. [Online]. Available: https://aclanthology.org/2021.emnlp-main.685
2021
Later among the works it cites.
N. Alshammari and S. Alanazi, “The impact of using different annotation schemes on named entity recognition,” Egyptian Informatics Journal , vol. 22, no. 3, pp. 295–302, 2021
2021
Later among the works it cites.
K. R. Chandu, Y. Bisk, and A. W. Black, “Grounding ‘grounding’ in NLP,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . Online: Association for Computational Linguistics, Aug. 2021, pp. 4283–4305. [Online]. Available: https://aclanthology.org/2021.findings-acl.375
2021
Later among the works it cites.
Z. Nasar, S. W. Jaffry, and M. K. Malik, “Named entity recognition and relation extraction: State-of-the-art,” ACM Comput. Surv. , vol. 54, no. 1, feb 2021. [Online]. Available: https://doi.org/10.1145/3445965
2021
Later among the works it cites.
Y. Tian, Y. Zhang, K.-J. Stol, L. Jiang, and H. Liu, “What makes a good commit message?” in Proceedings of the 44th International Conference on Software Engineering , ser. ICSE ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 2389–2401. [Online]. Available: https://doi.org/10.1145/3510003.3510205
2022
Later among the works it cites.
J. Dong, Y. Lou, Q. Zhu, Z. Sun, Z. Li, W. Zhang, and D. Hao, “Fira: Fine-grained graph-based code change representation for automated commit message generation,” in Proceedings of the 44th International Conference on Software Engineering , ser. ICSE ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 970–981. [Online]. Available: https://doi.org/10.1145/3510003.3510069
2022
Later among the works it cites.
S. Liu, C. Gao, S. Chen, L. Y. Nie, and Y. Liu, “Atom: Commit message generation based on abstract syntax tree and hybrid ranking,” IEEE Transactions on Software Engineering , vol. 48, no. 5, pp. 1800–1817, 2022
2022
Later among the works it cites.
E. Shi, Y. Wang, W. Tao, L. Du, H. Zhang, S. Han, D. Zhang, and H. Sun, “Race: Retrieval-augmented commit message generation,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022, pp. 5520–5530
2022
Later among the works it cites.
Y. Li, M. Soliman, and P. Avgeriou, “Identifying self-admitted technical debt in issue tracking systems using machine learning,” Empirical Software Engineering , vol. 27, no. 6, p. 131, 2022
2022
Later among the works it cites.
GitHub Inc., “Github rest api reference,” 2022, accessed: 2023-04-22. [Online]. Available: https://docs.github.com/en/rest?apiVersion=2022-11-28
2022
Later among the works it cites.
D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, and J. Yin, “UniXcoder: Unified cross-modal pre-training for code representation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 7212–7225. [Online]. Available: https://aclanthology.org/2022.acl-long.499
2022
Later among the works it cites.
H. Tian, X. Tang, A. Habib, S. Wang, K. Liu, X. Xia, J. Klein, and T. F. BissyandÉ, “Is this change the answer to that problem? correlating descriptions of bug and code changes for evaluating patch correctness,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’22. New York, NY, USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1145/3551349.3556914
2023
Closest in time.
Atlassian, “Jira: Issue and project tracking software,” 2021, accessed: 2023-04-22. [Online]. Available: https://www.atlassian.com/software/jira
2023
Closest in time.
Trello, Inc., “Trello: Team collaboration and management tool,” 2021, accessed: 2023-04-22. [Online]. Available: https://www.trello.com
2023
Closest in time.
GitHub Inc., “Github,” 2021, accessed: 2023-04-22. [Online]. Available: https://github.com
2023
Closest in time.
GitHub Inc., “Issues: Github’s issue tracking feature,” 2021, accessed: 2023-04-22. [Online]. Available: https://github.com/features/issues
2023
Closest in time.
PyGithub Contributors, “Pygithub: A python library to access the github api v3,” 2021, accessed: 2023-04-22. [Online]. Available: https://pygithub.readthedocs.io/en/latest/index.html
2023
Closest in time.
I. Heartex, “Label studio: Open source data labeling and annotation tool,” 2021, accessed: 2023-04-22. [Online]. Available: https://labelstud.io
2023
Closest in time.
Hugging Face, “Codeberta-small-v1 model,” 2023, accessed: 2023-04-30. [Online]. Available: https://huggingface.co/huggingface/CodeBERTa-small-v1
2023
Closest in time.
Hugging Face, “Hugging face: Natural language processing and machine learning platform,” 2021, accessed: 2023-04-22. [Online]. Available: https://huggingface.co
2023
Closest in time.
P. Loyola, E. Marrese-Taylor, and Y. Matsuo, “A neural architecture for generating natural language descriptions from source code changes,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 287–292. [Online]. Available: https://aclanthology.org/P17-2045
2045
Closest in time.