The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
On Automatically Generating Commit Messages via Summarization of Source Code Changes
Abstract
Although version control systems allow developers to describe and explain the rationale behind code changes in commit messages, the state of practice indicates that most of the time such commit messages are either very short or even empty. In fact, in a recent study of 23K+ Java projects it has been found that only 10% of the messages are descriptive and over 66% of those messages contained fewer words as compared to a typical English sentence (i.e., 15-20 words). However, accurate and complete commit messages summarizing software changes are important to support a number of development and maintenance tasks. In this paper we present an approach, coined as Change Scribe, which is designed to generate commit messages automatically from change sets. Change Scribe generates natural language commit messages by taking into account commit stereotype, the type of changes (e.g., files rename, changes done only to property files), as well as the impact set of the underlying changes. We evaluated Change Scribe in a survey involving 23 developers in which the participants analyzed automatically generated commit messages from real changes and compared them with commit messages written by the original developers of six open source systems. The results demonstrate that automatically generated messages by Change Scribe are preferred in about 62% of the cases for large commits, and about 54% for small commits.
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Citations
Cites
Towards automatically generating summary comments for Java methods
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., & Vijay-Shanker, K. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859006 · Cited by 514
Grissom, R. J., & Kim, J. J. (2012) · doi:10.4324/9780203803233 · Cited by 470 · cited 1×
“We also computed the Cliff's delta d effect size [12] to measure the magnitude of the difference.”
MethodAutomatically documenting program changes
Buse, R. P. L., & Weimer, W. R. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859005 · Cited by 219
Cited by
Software Documentation Issues Unveiled
Aghajani, E., Nagy, C., Vega-Marquez, O. L., Linares-Vasquez, M., Moreno, L., Bavota, G., & Lanza, M. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00122 · Cited by 195
Self-citationA Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
Loyola, P., Marrese-Taylor, E., & Matsuo, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2045 · Cited by 128
(2018) · doi:10.1145/3274404 · Cited by 122
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Liu, S., Gao, C., Chen, S., Nie, L. Y., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3038681 · Cited by 93
FIRA: fi ne-grained g ra ph-based code change representation for automated commit message generation
Dong, J., Lou, Y., Zhu, Q., Sun, Z., Li, Z., Zhang, W., & Hao, D. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510069 · Cited by 61
RACE: Retrieval-augmented Commit Message Generation
Shi, E., Wang, Y., Tao, W., Du, L., Zhang, H., Han, S., Zhang, D., & Sun, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.372 · Cited by 42
Correlating Automated and Human Evaluation of Code Documentation Generation Quality
Hu, X., Chen, Q., Wang, H., Xia, X., Lo, D., & Zimmermann, T. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3502853 · Cited by 33
Metadata
- Authors
- Cortes-Coy, L. F., Linares-Vasquez, M., Aponte, J., & Poshyvanyk, D.
- Venue
- 2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation
- Publisher
- IEEE
- Published
- 2014-09
- Pages
- 275-284
- OpenAlex
- W2019246026
- Cited by
- 164
- References
- 3
- FWCI
- 8.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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