@inproceedings{bai-etal-2021-jointly,
title = "Jointly Learning to Repair Code and Generate Commit Message",
author = "Bai, Jiaqi and
Zhou, Long and
Blanco, Ambrosio and
Liu, Shujie and
Wei, Furu and
Zhou, Ming and
Li, Zhoujun",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.771",
doi = "10.18653/v1/2021.emnlp-main.771",
pages = "9784--9795",
abstract = "We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.",
}
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<abstract>We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.</abstract>
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%0 Conference Proceedings
%T Jointly Learning to Repair Code and Generate Commit Message
%A Bai, Jiaqi
%A Zhou, Long
%A Blanco, Ambrosio
%A Liu, Shujie
%A Wei, Furu
%A Zhou, Ming
%A Li, Zhoujun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F bai-etal-2021-jointly
%X We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.
%R 10.18653/v1/2021.emnlp-main.771
%U https://aclanthology.org/2021.emnlp-main.771
%U https://doi.org/10.18653/v1/2021.emnlp-main.771
%P 9784-9795
Markdown (Informal)
[Jointly Learning to Repair Code and Generate Commit Message](https://aclanthology.org/2021.emnlp-main.771) (Bai et al., EMNLP 2021)
ACL
- Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, and Zhoujun Li. 2021. Jointly Learning to Repair Code and Generate Commit Message. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9784–9795, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.