Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2018
Abstract
Software engineers share experiences with modern technologies by means of software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. However, tags assigned to objects tend to be noisy and some objects are not well tagged. To improve the quality of tags in software information sites, we propose EnTagRec, an automatic tag recommender based on historical tag assignments to software objects and we evaluate its performance on four software information sites, Stack Overflow, Ask Ubuntu, Ask Different, and Free code. We observe that that EnTagRec achieves Recall@5 scores of 0.805, 0.815, 0.88 and 0.64, and Recall@10 scores of 0.868, 0.876, 0.944 and 0.753, on Stack Overflow, Ask Ubuntu, Ask Different, and Free code, respectively. In terms of Recall@5 and Recall@10, averaging across the 4 datasets, EnTagRec improves Tag Combine, which is the state of the art approach, by 27.3% and 12.9% respectively.
Keywords
Software information sites, Recommendation systems, Tagging
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Empirical Software Engineering
Volume
23
Issue
2
First Page
800
Last Page
832
ISSN
1382-3256
Identifier
10.1007/s10664-017-9533-1
Publisher
Springer Verlag (Germany)
Citation
Wang, Shaowei; LO, David; VASILESCU, Bogdan; and SEREBRENIK, Alexander.
EnTagRec(++): An enhanced tag recommendation system for software information sites. (2018). Empirical Software Engineering. 23, (2), 800-832.
Available at: https://ink.library.smu.edu.sg/sis_research/2428
Copyright Owner and License
Authors
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/10.1007/s10664-017-9533-1