Year: 2019
Type: article
Abstract: Code comments are a key software component containing information about the underlying implementation. Several studies have shown that code comments enhance the readability of the code. Nevertheless, not all the comments have the same goal and target audience. In this paper, we investigate how 14 diverse Java open and closed source software projects use code comments, with the aim of understanding their purpose. Through our analysis, we produce a taxonomy of source code comments; subsequently, we investigate how often each category occur by manually classifying more than 40,000 lines of code comments from the aforementioned projects. In addition, we investigate how to automatically classify code comments at line level into our taxonomy using machine learning; initial results are promising and suggest that an accurate classification is within reach, even when training the machine learner on projects different than the target one. Data and Materials [ https://doi.org/10.5281/zenodo.2628361 ].
Source: Empirical Software Engineering
Institutions: Delft University of Technology, University of Zurich
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,690
OpenAlex ID: W2941068928