Automatic Comment Generation via Multi-Pass Deliberation

作     者：Mu, Fangwen Chen, Xiao Shi, Lin Wang, Song Wang, Qing 

作者机构：Institute of Software Chinese Academy of Sciences Beijing China Lassonde School of Engineering York University Toronto Canada Laboratory for Internet Software Technologies Institute of Software CAS China University of Chinese Academy of Sciences China Science & Technology on Integrated Information System Laboratory Institute of Software CAS China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Deep neural networks 

摘      要：Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful. Copyright © 2022, The Authors. All rights reserved.