GitHub顶级存储库中活跃开发人员共同提交模式的大规模分析

Eldan Cohen, M. Consens
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引用次数: 11

摘要

GitHub是最大的代码托管网站(拥有2500万个公共活跃存储库和600万活跃用户的贡献),提供了一个前所未有的机会来观察软件开发人员的协作模式。理解社交编码现象背后的模式是一个活跃的研究领域,从中获得的见解可以指导更好的协作工具的设计,也可以帮助识别和选择开发人员人才。在本文中,我们对GitHub中的共同提交模式进行了大规模的分析。我们分析了20万开发人员在3年内使用17种最流行的编程语言向1.6万个存储库提交的1000万次提交。尽管我们的研究中包含了大量的数据,但我们密切关注存储库和开发人员的参与标准。我们根据声誉(基于星级排名)来选择存储库,我们在GitHub中引入了活跃开发人员的概念(观察到一小部分开发人员负责绝大多数提交)。使用共同作者网络,我们分析了每种编程语言的活动开发人员网络的共同提交模式。我们观察到,活跃的开发人员网络比一般的GitHub开发人员网络连接更少,更集中,并且不同语言的模式差异很大。我们将我们的结果与其他协作环境(Wikipedia和科学研究网络)进行比较,并且我们还描述了共同提交模式随时间的演变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Analysis of the Co-commit Patterns of the Active Developers in GitHub's Top Repositories
GitHub, the largest code hosting site (with 25 million public active repositories and contributions from 6 million active users), provides an unprecedented opportunity to observe the collaboration patterns of software developers. Understanding the patterns behind the social coding phenomena is an active research area where the insights gained can guide the design of better collaboration tools, and can also help to identify and select developer talent. In this paper, we present a large-scale analysis of the co-commit patterns in GitHub. We analyze 10 million commits made by 200 thousand developers to 16 thousand repositories, using 17 of the most popular programming languages over a period of 3 years. Although a large volume of data is included in our study, we pay close attention to the participation criteria for repositories and developers. We select repositories by reputation (based on star ranking), and we introduce the notion of active developer in GitHub (observing that a limited subset of developers is responsible for the vast majority of the commits). Using co-authorship networks, we analyze the co-commit patterns of the active developer network for each programming language. We observe that the active developer networks are less connected and more centralized than the general GitHub developer networks, and that the patterns vary significantly among languages. We compare our results to other collaborative environments (Wikipedia and scientific research networks), and we also describe the evolution of the co-commit patterns over time.
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