Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2022
Abstract
Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by interviewing and surveying practitioners about their expectations of research in code comment generation. We then compared what practitioners need and the current state-of-the-art research by performing a literature review of papers on code comment generation techniques published in the premier publication venues from 2010 to 2020. From this comparison, we highlighted the directions where researchers need to put effort to develop comment generation techniques that matter to practitioners.
Keywords
Code comment generation, Empirical study, Practitioners’ expectations
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering; Information Systems and Management
Publication
Proceedings of the 44th International Conference on Software Engineering, Pittsburgh, PA, USA, 2022 May 21-29
First Page
1693
Last Page
1705
Identifier
10.1145/3510003.3510152
Publisher
Association for Computing Machinery
City or Country
New York
Citation
HU, Xing; XIA, Xin; LO, David; WAN, Zhiyuan; CHEN, Qiuyuan; and ZIMMERMANN, Thomas.
Practitioners' expectations on automated code comment generation. (2022). Proceedings of the 44th International Conference on Software Engineering, Pittsburgh, PA, USA, 2022 May 21-29. 1693-1705.
Available at: https://ink.library.smu.edu.sg/sis_research/7687
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3510003.3510152