Year: 2019
Type: conference-paper
Abstract: Commit messages, which summarize the source code changes in natural language, are essential for program comprehension and software evolution understanding. Unfortunately, due to the lack of direct motivation, commit messages are sometimes neglected by developers, making it necessary to automatically generate such messages. State-of-the-art adopts learning based approaches such as neural machine translation models for the commit message generation problem. However, they tend to ignore the code structure information and suffer from the out-of-vocabulary issue. In this paper, we propose CoDiSum to address the above two limitations. In particular, we first extract both code structure and code semantics from the source code changes, and then jointly model these two sources of information so as to better learn the representations of the code changes. Moreover, we augment the model with copying mechanism to further mitigate the out-of-vocabulary issue. Experimental evaluations on real data demonstrate that the proposed approach significantly outperforms the state-of-the-art in terms of accurately generating the commit messages.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: CAREER: Network Robustification: Theories, Algorithms and Applications, G3442037871, G3543766647, III: Small: Towards a Theoretical Foundation for Diffusion Source Localization, 基于用户反馈的移动应用可信评估与演化技术研究, G6382532652, EAGER: Collaborative Research: Correspondence Discovery in Disparate Networks
OpenAlex ID: W2964772344