SCIENCE CHINA Information Sciences, Volume 62 , Issue 9 : 199105(2019) https://doi.org/10.1007/s11432-018-9822-1

A novel approach for recommending semantically linkable issues in GitHub projects

More info
  • ReceivedJul 12, 2018
  • AcceptedFeb 26, 2019
  • PublishedJul 29, 2019


There is no abstract available for this article.


This work was supported by National Grand RD Plan (Grant No. 2018YFB1003903) and National Natural Science Foundation of China (Grant No. 61432020).


[1] Zhang Y, Wang H M, Yin G. Social media in GitHub: the role of @-mention in assisting software development. Sci China Inf Sci, 2017, 60: 032102 CrossRef Google Scholar

[2] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems, 2013. 3111--3119. Google Scholar

[3] Le Q, Mikolov T. Distributed representations of sentences and documents. In: Proceedings of International Conference on Machine Learning, 2014. 1188--1196. Google Scholar

[4] Zhang Y, Yu Y, Wang H, Vasilescu B, and Filkov V. Within-ecosystem issue linking: a large-scale study of rails. In: Proceedings of International Workshop on Software Mining, 2018. 12--19. Google Scholar

[5] Kochhar P S, Xia X, Lo D, et al. Practitioners' expectations on automated fault localization. In: Proceedings of International Symposium on Software Testing and Analysis, 2016. 165--176. Google Scholar

[6] Zhou J, Zhang H, Lo D. Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports. In: Proceedings of International Conference on Software Engineering, 2012. 14--24. Google Scholar

[7] Rocha H, Valente M T, Marques-Neto H, et al. An empirical study on recommendations of similar bugs. In: Proceedings of International Conference on Software Analysis, Evolution, and Reengineering, 2016. 46--56. Google Scholar

  • Table 1   Performance comparison results. GH's SE: GitHub's search engine; IPM: improvements
    ProjectMetric Our approach GH's SE IPM (%)
    JqueryRr@1 0.228 0.086 165.1
    Rr@5 0.435 0.108 302.8
    MAP 0.161 0.061 163.9
    MRR 0.266 0.091 192.3
    RequestRr@1 0.194 0.083 133.7
    Rr@5 0.375 0.125 200.0
    MAP 0.140 0.063 122.2
    MRR 0.226 0.093 143.0