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Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction

机译:谁应对此拉取请求做出决定?分析时间衰减关系和文件相似性以进行集成商预测

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摘要

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests and integrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties in processing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships and File similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects containing 138,373 pull requests. Experimental results show that TRFPre makes accurate integrator predictions in terms of accuracies and Mean Reciprocal Rank. Less than 2 predictions are needed to find correct integrator in 91.67% of projects. In comparison with state-of-the-art approaches cHRev, WRC, TIE, CoreDevRec and ACRec, TRFPre improves top-1 accuracy by 68.2%, 73.9%, 49.3%, 14.3% and 46.4% on average across 24 projects. (C) 2019 Elsevier Inc. All rights reserved.
机译:在基于拉式的开发模型中,集成商负责决定是否接受拉式请求和集成代码贡献。理想情况下,将拉取请求分配给集成商,并在提交后的短时间内对其进行评估。但是,在流行的项目中,传入请求请求的数量很大,并且集成者在及时处理请求请求时经常遇到困难。因此,需要一种自动的积分器预测方法来将适当的拉取请求分配给积分器。在本文中,我们提出了一种TRFPre方法,该方法可以分析时间衰减关系和文件相似性来预测积分器。我们评估TRFPre在24个项目中的有效性,这些项目包含138,373个拉动请求。实验结果表明,TRFPre可根据准确度和平均倒数排名做出准确的积分预测。在91.67%的项目中,只需不到2个预测就可以找到正确的集成商。与最新的方法cHRev,WRC,TIE,CoreDevRec和ACRec相比,TRFPre在24个项目中平均将top-1准确性提高了68.2%,73.9%,49.3%,14.3%和46.4%。 (C)2019 Elsevier Inc.保留所有权利。

著录项

  • 来源
    《The Journal of Systems and Software》 |2019年第8期|196-210|共15页
  • 作者单位

    Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China;

    Singapore Management Univ, Sch Informat Syst, Singapore, Singapore;

    Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China;

    Monash Univ, Informat Technol, Melbourne, Vic, Australia;

    Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China;

    Beihang Univ, State Key Lab Software Dev Environm, Beijing, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Integrator prediction; Code review; Open source; Github;

    机译:集成商预测;代码审查;开源;Github;

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