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Home > Archive>Volume 30, Issue 5, 2019 >1407-1421. DOI:10.13328/j.cnki.jos.005732
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Hybrid Approach for Linking Related Issues Based on Embedding Models
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National Key Research and Development Program of China (2018YFB1003903); National Natural Science Foundation of China (61432020)

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    Abstract:

    Social coding facilitates the sharing of knowledge in Open-source community. In particular, issue reports, as an important knowledge in the software development, usually contain relevant information, and can thus be linked to other related issues manually. In a project, identifying and linking issues to potentially related issues would provide developers more targeted resource and information when they resolve target issues, thus improving the issue resolution efficiency. However, the current manual linking approach is in general time-consuming and mainly depends on the experience and knowledge of the individual developers. Therefore, investigating how to link related issues timely is a meaningful task which can improve development efficiency of open-source projects. In this study, the problem of linking related issues is formulated as a recommendation problem and a hybrid approach based on embedding models is proposed, combining the traditional information retrieval technique, i.e., TF-IDF, and the embedding models in deep learning techniques, i.e., word embedding and document embedding. The evaluation results show that, the proposed approach can improve the performance of traditional approaches, with a very strong application scalability.

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张洋,王涛,吴逸文,尹刚,王怀民.基于嵌入模型的混合式相关缺陷关联方法.软件学报,2019,30(5):1407-1421

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History
  • Received:September 01,2018
  • Revised:October 31,2018
  • Online: May 08,2019
Links:Institute of Software, CASChina Computer FederationCASNSFC
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