实践中的总线因素

Elgun Jabrayilzade, Mikhail Evtikhiev, Eray Tüzün, V. Kovalenko
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引用次数: 4

摘要

总线因子是一种度量,用于确定项目对工程师突然离职的弹性有多大。它规定了一个项目被公共汽车撞到的最少工程师人数。尽管这个度量标准在社区中经常被讨论,但很少有研究考虑到它的普遍相关性。此外,现有的总线因子估计工具只关注版本控制系统的数据,尽管存在其他的知识生成和分发渠道。通过对269名工程师的调查,我们发现总线因素被认为是集体开发中的一个重要问题,并确定了软件开发团队中知识生成和分布的最高影响渠道。我们还提出了一种多模式总线因子估计算法,该算法将代码审查和会议数据与VCS数据一起使用。我们在JetBrains开发的13个项目中测试了该算法,并将其结果与Avelino等人最先进的工具的结果进行了比较,并将其与在这些项目中工作的工程师调查中收集的基本事实进行了比较。与Avelino等人的结果相比,我们的算法在预测总线因子和关键开发人员方面稍好一些。最后,我们通过访谈和调查得出了一组解决公共因素问题的最佳实践,并为可能的公共因素评估工具提出了建议。
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Bus Factor in Practice
Bus factor is a metric that identifies how resilient is the project to the sudden engineer turnover. It states the minimal number of engineers that have to be hit by a bus for a project to be stalled. Even though the metric is often discussed in the community, few studies consider its general relevance. Moreover, the existing tools for bus factor estimation focus solely on the data from version control systems, even though there exists other channels for knowledge generation and distribution. With a survey of 269 engineers, we find that the bus factor is perceived as an important problem in collective development, and determine the highest impact channels of knowledge generation and distribution in software development teams. We also propose a multimodal bus factor estimation algorithm that uses data on code reviews and meetings together with the VCS data. We test the algorithm on 13 projects developed at JetBrains and compared its results to the results of the state-of-the-art tool by Avelino et al. against the ground truth collected in a survey of the engineers working on these projects. Our algorithm is slightly better in terms of both predicting the bus factor as well as key developers compared to the results of Avelino et al. Finally, we use the interviews and the surveys to derive a set of best practices to address the bus factor issue and proposals for the possible bus factor assessment tool.
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