Cited By
View all- Vu TBui TDo TNguyen TVo HNguyen S(2025)Automated description generation for software patchesInformation and Software Technology10.1016/j.infsof.2024.107543177(107543)Online publication date: Jan-2025
KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
Authors: 
Wei Tao, Yucheng Zhou, Yanlin Wang, Hongyu Zhang, Haofen Wang, Wenqiang ZhangAuthors Info & Claims
Wei Tao, Yucheng Zhou, Yanlin Wang, Hongyu Zhang, Haofen Wang, Wenqiang ZhangAuthors Info & ClaimsArticle No.: 133, Pages 1 - 32
Abstract
Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods.
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Index Terms
- KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
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Publication History
Published: 04 June 2024
Online AM: 29 January 2024
Accepted: 15 January 2024
Revised: 29 November 2023
Received: 20 July 2023
Published in TOSEM Volume 33, Issue 5
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View all- Vu TBui TDo TNguyen TVo HNguyen S(2025)Automated description generation for software patchesInformation and Software Technology10.1016/j.infsof.2024.107543177(107543)Online publication date: Jan-2025
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