9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay.
Saved in:
| Title: | 9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay. |
|---|---|
| Authors: | Hideaki Hata, Treude, Christoph, Kula, Raula Gaikovina, Takashi Ishio |
| Source: | ICSE: International Conference on Software Engineering; 5/25/2019, p1211-1221, 11p |
| Subject Terms: | SOURCE code, ARTIFICIAL intelligence, COMPUTER science, SOFTWARE engineering, COMPUTER software development |
| Abstract: | Links are an essential feature of the World Wide Web, and source code repositories are no exception. However, despite their many undisputed benefits, links can suffer from decay, insufficient versioning, and lack of bidirectional traceability. In this paper, we investigate the role of links contained in source code comments from these perspectives. We conducted a large-scale study of around 9.6 million links to establish their prevalence, and we used a mixed-methods approach to identify the links' targets, purposes, decay, and evolutionary aspects. We found that links are prevalent in source code repositories, that licenses, software homepages, and specifications are common types of link targets, and that links are often included to provide metadata or attribution. Links are rarely updated, but many link targets evolve. Almost 10% of the links included in source code comments are dead. We then submitted a batch of link-fixing pull requests to open source software repositories, resulting in most of our fixes being merged successfully. Our findings indicate that links in source code comments can indeed be fragile, and our work opens up avenues for future work to address these problems. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Be the first to leave a comment!