论文标题

开源项目中的代码审查:性别偏见如何影响参与和结果?

Code Reviews in Open Source Projects : How Do Gender Biases Affect Participation and Outcomes?

论文作者

Sultana, Sayma, Turzo, Asif Kamal, Bosu, Amiangshu

论文摘要

背景:当代软件开发组织缺乏多样性,自由和开源软件(FOSS)社区中女性的比率甚至低于行业平均水平。尽管最近的研究结果暗示了针对妇女的偏见,但尚不清楚这种偏见在多大程度上影响了各种软件开发任务的结果。 目的:我们的目的是确定是否或参与代码审查(或拉的请求)的结果受开发人员的性别影响。 方法:以此目标,这项研究包括总计1010个FOSS项目。我们为14个数据集(即基于10 Gerrit和4个GitHub)的每个回归模型开发了六个回归模型,以确定代码接受,审查间隔和代码审查参与者是否根据开发人员的性别和性别中性概况而有所不同。 主要发现:我们的结果在14个数据集中的13个中发现了法规接受期间的重大性别偏见,其中7个偏爱男性和其余6名受青睐的女性。我们还发现,在代码审查间隔方面,男性和女性之间的差异很大,女性在三例案件中遇到较长的延误,而在七个情况下则相反。我们的结果表明,审核者的选择是大多数项目中最偏见的方面之一,而女性在14个案件中有11例中的代码审查参与大大降低。由于大多数审查作业是基于邀请,因此该结果表明开发人员可能存在亲和力偏见。 结论:尽管在许多项目中存在性别偏见,但偏见的方向和振幅会根据项目规模,社区和文化而有所不同。由于偏见及其根本原因的特征不同,因此类似的缓解策略可能并不是在所有社区中起作用。

Context: Contemporary software development organizations lack diversity and the ratios of women in Free and open-source software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint the existence of biases against women, it is unclear to what extent such biases influence the outcomes of various software development tasks. Aim: We aim to identify whether the outcomes of or participation in code reviews (or pull requests) are influenced by the gender of a developer.. Approach: With this goal, this study includes a total 1010 FOSS projects. We developed six regression models for each of the 14 dataset (i.e., 10 Gerrit based and four Github) to identify if code acceptance, review intervals, and code review participation differ based on the gender and gender neutral profile of a developer. Key findings: Our results find significant gender biases during code acceptance among 13 out of the 14 datasets, with seven seven favoring men and the remaining six favoring women. We also found significant differences between men and women in terms of code review intervals, with women encountering longer delays in three cases and the opposite in seven. Our results indicate reviewer selection as one of the most gender biased aspects among most of the projects, with women having significantly lower code review participation among 11 out of the 14 cases. Since most of the review assignments are based on invitations, this result suggests possible affinity biases among the developers. Conclusion: Though gender bias exists among many projects, direction and amplitude of bias varies based on project size, community and culture. Similar bias mitigation strategies may not work across all communities, as characteristics of biases and their underlying causes differ.

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