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

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Abstract

Communication and coordination between open source software (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-the-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 contributor 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 the 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 to 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 with 54.8% using only textual similarity and 78.2% using only change similarity.

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Authors and Affiliations

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

    Zhi-Xing Li, Yue Yu, Tao Wang, Gang Yin & Huai-Min Wang

  2. Laboratory of Software Engineering for Complex Systems, College of Computer, National University of Defense Technology, Changsha, 410073, China

    Xin-Jun Mao

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  1. Zhi-Xing Li
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  2. Yue Yu
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Correspondence to Yue Yu.

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Li, ZX., Yu, Y., Wang, T. et al. Detecting Duplicate Contributions in Pull-Based Model Combining Textual and Change Similarities. J. Comput. Sci. Technol. 36, 191–206 (2021). https://doi.org/10.1007/s11390-020-9935-1

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