›› 2015,Vol. 30 ›› Issue (5): 998-1016.doi: 10.1007/s11390-015-1577-3

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

CoreDevRec:自动分配核心成员进行贡献评估

Jing Jiang(蒋竞), Member, CCF, Jia-Huan He(贺佳欢), Xue-Yuan Chen(陈学渊)   

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
  • 收稿日期:2015-03-19 修回日期:2015-07-16 出版日期:2015-09-05 发布日期:2015-09-05
  • 作者简介:Jing Jiang received her B.S. and Ph.D. degrees in computer science from Peking University, Beijing, in 2007 and 2012, respectively. She is now an assistant professor in the State Key Laboratory of Software Development Environment of Beihang University, Beijing. Her research interests include software engineering, data mining, human factors and social aspects of software engineering.
  • 基金资助:

    This work is supported by the National Natural Science Foundation of China under Grant No. 61300006 and the State Key Laboratory of Software Development Environment of China under Grant No. SKLSDE-2015ZX-24.

CoreDevRec: Automatic Core Member Recommendation for Contribution Evaluation

Jing Jiang(蒋竞), Member, CCF, Jia-Huan He(贺佳欢), Xue-Yuan Chen(陈学渊)   

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
  • Received:2015-03-19 Revised:2015-07-16 Online:2015-09-05 Published:2015-09-05
  • About author:Jing Jiang received her B.S. and Ph.D. degrees in computer science from Peking University, Beijing, in 2007 and 2012, respectively. She is now an assistant professor in the State Key Laboratory of Software Development Environment of Beihang University, Beijing. Her research interests include software engineering, data mining, human factors and social aspects of software engineering.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant No. 61300006 and the State Key Laboratory of Software Development Environment of China under Grant No. SKLSDE-2015ZX-24.

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基于拉模式的软件开发使贡献代码变得灵活而高效。贡献者向项目提交变更的代码, 项目核心成员评估变更代码并决定是否将其集成到代码库。理想情况下, 变更代码在提交后很快被分配给核心成员进行评估。然而实际上, 一些热门项目收到大量的贡献请求, 核心成员难以选择将要评估的贡献请求。因此, 越来越需要一种自动推荐核心成员的方法, 从而改善评估过程。本文研究人工分配的贡献请求。结果显示3.2%~40.6%的贡献请求会被人工分配给特定的核心成员。为了协助人工分配, 本文在GitHub上设计了自动推荐负责贡献评估的核心成员的方法CoreDevRec。CoreDevRec使用支持向量机分析多维属性, 包括修改代码的文件路径、贡献者与核心成员间的关系以及核心成员的活跃度等。本文使用GitHub上5个热门项目的18651条贡献请求, 对CoreDevRec进行实验评估。实验结果显示推荐前三名核心成员时, CoreDevRec的准确率为72.9%~93.5%。与基准方法相比, CoreDevRe将准确率提高了18.7%~81.3%。在项目TrinityCore中, CoreDevRec的准确率甚至高于人工分配。因此, 本文相信CoreDevRec可以改善贡献请求分配过程。

Abstract: The pull-based software development helps developers make contributions flexibly and efficiently. Core members evaluate code changes submitted by contributors, and decide whether to merge these code changes into repositories or not. Ideally, code changes are assigned to core members and evaluated within a short time after their submission. However, in reality, some popular projects receive many pull requests, and core members have difficulties in choosing pull requests which are to be evaluated. Therefore, there is a growing need for automatic core member recommendation, which improves the evaluation process. In this paper, we investigate pull requests with manual assignment. Results show that 3.2%~40.6% of pull requests are manually assigned to specific core members. To assist with the manual assignment, we propose CoreDevRec to recommend core members for contribution evaluation in GitHub. CoreDevRec uses support vector machines to analyze different kinds of features, including file paths of modified codes, relationships between contributors and core members, and activeness of core members. We evaluate CoreDevRec on 18651 pull requests of five popular projects in GitHub. Results show that CoreDevRec achieves accuracy from 72.9% to 93.5% for top 3 recommendation. In comparison with a baseline approach, CoreDevRec improves the accuracy from 18.7% to 81.3% for top 3 recommendation. Moreover, CoreDevRec even has higher accuracy than manual assignment in the project TrinityCore. We believe that CoreDevRec can improve the assignment of pull requests.

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