RACE: Retrieval-augmented Commit Message Generation

Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

Abstract
Commit messages are important for software development and maintenance. Many neural network-based approaches have been proposed and shown promising results on automatic commit message generation. However, the generated commit messages could be repetitive or redundant. In this paper, we propose RACE, a new retrieval-augmented neural commit message generation method, which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. As the retrieved commit message may not always accurately describe the content/intent of the current code diff, we also propose an exemplar guider, which learns the semantic similarity between the retrieved and current code diff and then guides the generation of commit message based on the similarity. We conduct extensive experiments on a large public dataset with five programming languages. Experimental results show that RACE can outperform all baselines. Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
Anthology ID:
2022.emnlp-main.372
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5520–5530
Language:
URL:
https://aclanthology.org/2022.emnlp-main.372
DOI:
10.18653/v1/2022.emnlp-main.372
Bibkey:
Cite (ACL):
Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Hongbin Sun. 2022. RACE: Retrieval-augmented Commit Message Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5520–5530, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
RACE: Retrieval-augmented Commit Message Generation (Shi et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.372.pdf
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