Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (1): 191-206.doi: 10.1007/s11390-020-9935-1

Special Issue: Software Systems

• Regular Paper • Previous Articles     Next Articles

Detecting Duplicate Contributions in Pull-based Model Combining Textual and Change Similarities

Zhi-Xing Li1, Yue Yu1,*, Member, CCF, ACM, Tao Wang1, Member, CCF, ACM Gang Yin1, Member, CCF, ACM, Xin-Jun Mao2, Member, CCF, ACM, and Huai-Min Wang1, Fellow, CCF, ACM        

  1. 1 Key Laboratory of Parallel and Distributed Computing, College of Computer, National University of Defense Technology Changsha 410073, China;
    2 Laboratory of Software Engineering for Complex Systems, College of Computer, National University of Defense Technology, Changsha 410073, China
  • Received:2019-08-15 Revised:2020-01-03 Online:2021-01-05 Published:2021-01-23
  • Contact: Yue Yu E-mail:yuyue@nudt.edu.cn
  • About author:Zhi-Xing Li is a Ph.D. candidate in the College of Computer at National University of Defense Technology (NUDT), Changsha. He received his Master's degree in computer science from NUDT, Changsha, in 2017. His work interests include software engineering, data mining, and knowledge discovering in open source communities.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004202, and the National Natural Science Foundation of China under Grant No. 61702534.

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Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging issues. The pull-based development model, as the state-of-art collaborative development mechanism, provides high openness and transparency to improve the visibility of contributors' work. However, duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this model. If not detected in time, duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work. In this paper, we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission time. For a new-arriving contribution, we first compute textual similarity and change similarity between it and other existing contributions. And then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change similarity. The evaluation shows that 83.4% of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8% using only textual similarity and 78.2% using only change similarity.

Key words: Pull-request; Duplicate detection; textual similarity; change similarity;

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