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FastTagRec: fast tag recommendation for software information sites

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Abstract

Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers’ understanding of their own postings but also on other factors, including developers’ English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.

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Acknowledgements

The authors would like to acknowledge the support provided by the National Key Research and Development Program of China (2017YFB1400602, 2018YFB1003800), the National Natural Science Foundation of China (61572374, U163620068, 61472423), Open Fund of Key Laboratory of Network Assessment Technology from CAS, and Academic Team Building Plan for Young Scholars from Wuhan University (WHU2016012).

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, China

    Jin Liu & Pingyi Zhou

  2. Key Laboratory Of Network Assessment Technology, Institute Of Information Engineering, Chinese Academy Of Sciences, Beijing, China

    Jin Liu

  3. Department of Computer Science, Western Michigan University, Kalamazoo, MI, USA

    Zijiang Yang

  4. School of Information Technology, Deakin University, Melbourne, Australia

    Xiao Liu

  5. Faculty of Information Technology, Monash University, Melbourne, Australia

    John Grundy

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  1. Jin Liu
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Correspondence to Jin Liu.

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Liu, J., Zhou, P., Yang, Z. et al. FastTagRec: fast tag recommendation for software information sites. Autom Softw Eng 25, 675–701 (2018). https://doi.org/10.1007/s10515-018-0239-4

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