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Tag recommendation for open source software

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Abstract

Nowadays open source software becomes highly popular and is of great importance for most software engineering activities. To facilitate software organization and retrieval, tagging is extensively used in open source communities. However, finding the desired software through tags in these communities such as Freecode and ohloh is still challenging because of tag insufficiency. In this paper, we propose TRG (tag recommendation based on semantic graph), a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effective algorithm to recommend tags for software. With comprehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.

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Author information

Authors and Affiliations

  1. National Laboratory for Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha, 410073, China

    Tao Wang, Huaimin Wang, Gang Yin, Xiao Li & Peng Zou

  2. Department of Computer Science, The University of Western Ontario, London, N6A5B7, Canada

    Charles X. Ling & Xiao Li

  3. Academy of Equipment, Beijing, 101400, China

    Peng Zou

Authors
  1. Tao Wang
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  2. Huaimin Wang
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  3. Gang Yin
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  4. Charles X. Ling
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  5. Xiao Li
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  6. Peng Zou
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Corresponding author

Correspondence to Tao Wang.

Additional information

Tao Wang received both his BS and MS in Computer Science from National University of Defense Technology (NUDT) in 2007 and 2010. He is now a PhD candidate in Computer Science, NUDT. His work interests include open source software engineering, machine learning, data mining, and knowledge discovering in open source software.

Huaimin Wang received his Ph D in Computer Science from National University of Defense Technology (NUDT) in 1992. He is now a professor and chief engineer in department of educational affairs, NUDT. He has been awarded the “Chang Jiang Scholars Program” professor and the Distinct Young Scholar, etc. He has published more than 100 research papers in peer-reviewed international conferences and journals. His current research interests include middleware, software agent, and trustworthy computing.

Gang Yin received his Ph D degree in Computer Science from National University of Defense Technology (NUDT) in 2006. He is now an associate professor in NUDT. He has worked in several grand research projects including national 973, 863 projects and so on. He has published more than 60 research papers in international conferences and journals. His current research interests include distributed computing, information security, software engineering, and machine learning.

Charles X. Ling earned both of his MS and PhD from Computer and Information Science at University of Pennsylvania, and now a faculty member in Computer Science at Western University. He was/is an Associate Editor of IEEE TKDE, ACM TIST as well as the Panel Co-chair of ACM SIGKDD’12 and so on. He has published over 120 research papers in peer-reviewed conferences and journals such as IJCAI, TKDE. He is a Senior Member of IEEE and Lifetime Member of AAAI.

Xiao Li received the BS and MS degrees in Computer Science at the National University of Defense Technology in 2006 and 2008. He is currently a PhD Candidate in the Department of Computer Science at The University of Western Ontario. His research interests include data mining, machine learning, and related real-world applications.

Peng Zou is a professor, PhD supervisor in National University of Defense Technology, and now works in Academy of Equipment. He has worked as the director of several grand research projects and published many research papers in peer-reviewed international conferences and journals. His research interests include network, information security, distributed computing, and software engineering.

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Wang, T., Wang, H., Yin, G. et al. Tag recommendation for open source software. Front. Comput. Sci. 8, 69–82 (2014). https://doi.org/10.1007/s11704-013-2394-x

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