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RevRec: A two-layer reviewer recommendation algorithm in pull-based development model

RevREC:一个基于Pull-Request 开发模型的双层审阅人推荐算法

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Abstract

Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests (PRs) in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.

摘要

代码审查是减少代码缺陷和提高软件质量的重要过程。在像GitHub 这样的社交编码社区,由 于每个人都可以提交Pull-Request,所以代码审查扮演着比以往更重要的角色,而且这个过程非常耗 时。因此,寻找并推荐正确的评审人员来应对新兴的Pull-Request 成为一项重要任务。然而,目前大 部分的研究主要集中在评估人员是否参与,并没有对人员参与的类型进行区分。在本文中,我们开发 了一个两层审阅人推荐模型,从技术和管理角度为GitHub 项目中的Pull-Request(PR)推荐审阅人。对 于第一层,我们根据混合推荐方法推荐合适的审阅人对目标PR 进行审阅。对于第二层,在从第一层 获得推荐结果之后,我们指定被推荐的审阅人是技术还是管理上参与审阅过程。我们在GitHub 的两 个热门项目上进行了实验,并使用2016 年2 月至2017 年2 月期间创建的PR 来测试该方法。结果显 示,我们的推荐模型的第一层比以前的工作表现得更好,第二层可以有效地区分参与类型。

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Authors and Affiliations

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

    Cheng Yang  (杨程), Xun-hui Zhang  (张迅晖), Ling-bin Zeng  (曾令斌), Qiang Fan  (范强), Tao Wang  (王涛), Yue Yu  (余跃), Gang Yin  (尹刚) & Huai-min Wang  (王怀民)

Authors
  1. Cheng Yang  (杨程)
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  2. Xun-hui Zhang  (张迅晖)
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  3. Ling-bin Zeng  (曾令斌)
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  4. Qiang Fan  (范强)
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  5. Tao Wang  (王涛)
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  6. Yue Yu  (余跃)
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  7. Gang Yin  (尹刚)
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  8. Huai-min Wang  (王怀民)
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Correspondence to Cheng Yang  (杨程).

Additional information

Foundation item: Project(2016-YFB1000805) supported by the National Grand R&D Plan, China; Projects(61502512, 61432020, 61472430, 61532004) supported by the National Natural Science Foundation of China

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Yang, C., Zhang, Xh., Zeng, Lb. et al. RevRec: A two-layer reviewer recommendation algorithm in pull-based development model. J. Cent. South Univ. 25, 1129–1143 (2018). https://doi.org/10.1007/s11771-018-3812-x

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  • DOI: https://doi.org/10.1007/s11771-018-3812-x

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