Automatically detecting the scopes of source code comments

• Published in: The Journal of systems and software 2019-07, Vol.153, p.45-63
• Main Authors: Chen, Huanchao, Huang, Yuan, Liu, Zhiyong, Chen, Xiangping, Zhou, Fan, Luo, Xiaonan
• Format: Article
• Language: English
• Subjects: Comment scope detection; Machine learning; Software repositories
• ISSN: 0164-1212; 1873-1228
• DOI: 10.1016/j.jss.2019.03.010

•This work proposes the first method to detect the scopes of block/line comments•The detection of comment scopes can be modeled as a binary classification problem•We propose discriminative features in code and comments to characterize the scopes of comments.•We apply our method to two existing approaches in software engineering tasks and improve their performance. Comments convey useful information about the system functionalities and many methods for software engineering tasks take comments as an important source for many software engineering tasks such as code semantic analysis, code reuse and so on. However, unlike structural doc comments, it is challenging to identify the relationship between the functional semantics of the code and its corresponding textual descriptions nested inside the code and apply it to automatic analyzing and mining approaches in software engineering tasks efficiently. In this paper, we propose a general method for the detection of source code comment scopes. Based on machine learning, our method utilized features of code snippets and comments to detect the scopes of source code comments automatically in Java programs. On the dataset of comment-statement pairs from 4 popular open source projects, our method achieved a high accuracy of 81.45% in detecting the scopes of comments. Furthermore, the results demonstrated the feasibility and effectiveness of our comment scope detection method on new projects. Moreover, our method was applied to two specific software engineering tasks in our studies: analyzing software repositories for outdated comment detection and mining software repositories for comment generation. As a general approach, our method provided a solution to comment-code mapping. It improved the performance of baseline methods in both tasks, which demonstrated that our method is conducive to automatic analyzing and mining approaches on software repositories.