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.
Software information sites, Recommendation systems, Tagging
Software and Cyber-Physical Systems
Empirical Software Engineering
Springer Verlag (Germany)
Wang, Shaowei; LO, David; Vasilescu, Bogdan; and Serebrenik, Alexander.
EnTagRec: An enhanced tag recommendation system for software information sites. (2017). Empirical Software Engineering. 1-33. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2428
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