Abstract
Software information sites such as Stack Overflow and Ask Ubuntu allow programmers to post their questions and share knowledge online. Usually tags that describe the key content of the questions are required by the website. These tags play an important role in organizing and indexing user posts efficiently and provide accurate abstracts of complicated technical problems. Users attach tags to the questions according to their experience and knowledge. Due to the expression difference and lack of grasp of the software, choosing the accurate tags is not an easy job. In this paper, we propose CUT, an automatic tag recommendation approach which recommends appropriate tags after users post their questions. This approach incorporates code fragments, text content, users’ preference to tags and tag relation in recommendation process. We evaluated CUT by conducting comparative experiments on the Stack Overflow dataset. The results show that CUT achieves 69.9 % and 81.6 % respectively for recall@5 and recall@10, which outperforms the latest relevant approach.
Y. Li—The work is supported by Key Program of National Natural Science Foundation of China (Grant No. 61232005), and VMware UR project.
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Yang, Y., Li, Y., Yue, Y., Wu, Z., Shao, W. (2016). CUT: A Combined Approach for Tag Recommendation in Software Information Sites. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_47
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