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开源软件生态系统的开发者角色演化:以GNOME为例的解释性研究

Developer Role Evolution in Open Source Software Ecosystem: An Explanatory Study on GNOME

  • 摘要: 开源软件生态系统是由众多项目及其开发者组成的大型开源社区。项目和开发者在同一个生态系统中共同演化。为了保证开源生态系统的健康演化,我们需要吸引新开发者和维持已有开发者,特别是在团队中起到了重要作用的核心开发者和项目领导。因此,找出能够影响开发者演化成核心开发者或者项目领导的因素非常重要。为了找出这些因素,我们在GNOME生态系统上开展了案例研究。首先,我们收集了可能反映开发者主观意愿和开发者所处项目环境的指标。其次,我们基于GNOME的数据集计算出这些指标。接着,在去掉共线性严重的指标之后,我们使用剩余的指标拟合了Logistic回归模型,其中自变量是剩余指标,因变量是开发者的未来角色。结果显示,一部分主观意愿和项目环境的指标(例如开发者参加的项目总数)显著影响了开发者成为核心开发者或者项目领导的机会。另外在不同的验证方法下,我们获得的模型能够很好的预测开发型核心开发者,预测的精准度稳定在77%(F值)。

     

    Abstract: An open source software (OSS) ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers' chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers' subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role (the core developer or project leader). The results showed that part of such indicators (e.g., the total number of projects that a developer joined) of subjective willingness and project environment significantly influenced the developers' chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance (0.770, F-value).

     

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