重访亚里士多德与林格尔曼之争：偏差对开源软件开发中生产力测量的影响
Revisiting Aristotle vs. Ringelmann: The influence of biases on measuring productivity in Open Source software development
AI总结：
本文通过复现苏黎世联邦理工学院两个团队关于开源软件生产力与团队规模关系的研究，发现抽样偏差只能部分解释其相反结论，仪器偏差才是造成差异的主要原因。
摘要：
亚里士多德与林格尔曼之争是来自苏黎世联邦理工学院的两个不同研究团队之间的一场讨论，他们争论的是开源软件项目的生产力相对于团队规模是呈次线性还是超线性增长。这场讨论围绕两篇论文展开，这两篇论文显然使用了类似的技术：在GitHub上对项目进行抽样，并运行回归分析来回答关于超线性的问题。尽管研究方法相似，Ingo Scholtes带领的团队得出的结论是项目呈次线性增长，而Didier Sornette带领的另一个团队则确定团队规模与生产力之间存在超线性关系。在后续的论文中，两位作者认为相反的结论可能归因于项目总体的差异，因为Sornette的项目中有81.7%的贡献者少于50人。而Scholtes则专门抽样了贡献者超过50人的项目。本文通过复现两位作者的发现来比较他们的研究，从而评估项目抽样在多大程度上实际造成了Scholtes与Sornette结果之间的差异。由此发现，抽样偏差只能部分解释两位作者之间的差异。进一步的分析发现了仪器偏差，这些偏差将回归系数推向相反的方向。这些发现随后被整合到一个定量分析中，表明仪器偏差对Scholtes与Sornette工作之间差异的贡献比两位作者所提出的选择偏差更大。
英文摘要：
Aristotle vs. Ringelmann was a discussion between two distinct research teams from the ETH Zürich who argued whether the productivity of Open Source software projects scales sublinear or superlinear with regard to its team size. This discussion evolved around two publications, which apparently used similar techniques by sampling projects on GitHub and running regression analyses to answer the question about superlinearity. Despite the similarity in their research methods, one team around Ingo Scholtes reached the conclusion that projects scale sublinear, while the other team around Didier Sornette ascertained a superlinear relationship between team size and productivity. In subsequent publications, the two authors argue that the opposite conclusions may be attributed to differences in project populations, since 81.7% of Sornette's projects have less than 50 contributors. Scholtes, on the other hand, sampled specifically projects with more than 50 contributors. This publication compares the research from both authors by replicating their findings, thus allowing for an evaluation of how much project sampling actually accounted for the differences between Scholtes' and Sornette's results. Thereby, the discovery was made that sampling bias only partially explains the discrepancies between the two authors. Further analysis led to the detection of instrumentation biases that drove the regression coefficients in opposite directions. These findings were then consolidated into a quantitative analysis, indicating that instrumentation biases contributed more to the differences between Scholtes' and Sornette's work than the selection bias suggested by both authors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
