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Can Cheng, Bing Li, Zeng-Yang Li, Yu-Qi Zhao, Feng-Ling Liao. Developer Role Evolution in Open Source Software Ecosystem: An Explanatory Study on GNOME[J]. Journal of Computer Science and Technology, 2017, 32(2): 396-414. DOI: 10.1007/s11390-017-1728-9
Citation: Can Cheng, Bing Li, Zeng-Yang Li, Yu-Qi Zhao, Feng-Ling Liao. Developer Role Evolution in Open Source Software Ecosystem: An Explanatory Study on GNOME[J]. Journal of Computer Science and Technology, 2017, 32(2): 396-414. DOI: 10.1007/s11390-017-1728-9

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

  • 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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