Software productivity has always been one of the most critical metrics for measuring software development.However,with the open-source community(e.g.,GitHub),new software development models are emerging.The traditiona...Software productivity has always been one of the most critical metrics for measuring software development.However,with the open-source community(e.g.,GitHub),new software development models are emerging.The traditional productivity metrics do not provide a comprehensive measure of the new software development models.Therefore,it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community’s production activities.Based on the natural ecosystem,this paper proposes concepts related to the productivity of open source software ecosystems,analyses influencing factors of open source software ecosystem productivity,and constructs a measurement model using these factors.Model validation experiments show that the model is compatible with a large portion of open source software ecosystems in GitHub.This study can provide references for participants of the open-source software ecosystem to choose proper types of ecosystems.The study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.展开更多
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....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）.展开更多
基金supported in part by the National Key R&D Program of China under Grant No.2018YFB1003800.
文摘Software productivity has always been one of the most critical metrics for measuring software development.However,with the open-source community(e.g.,GitHub),new software development models are emerging.The traditional productivity metrics do not provide a comprehensive measure of the new software development models.Therefore,it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community’s production activities.Based on the natural ecosystem,this paper proposes concepts related to the productivity of open source software ecosystems,analyses influencing factors of open source software ecosystem productivity,and constructs a measurement model using these factors.Model validation experiments show that the model is compatible with a large portion of open source software ecosystems in GitHub.This study can provide references for participants of the open-source software ecosystem to choose proper types of ecosystems.The study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.
基金This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB0800400, the National Basic Research 973 Program of China under Grant No. 2014CB340404, the National Natural Science Foundation of China under Grant Nos. 61572371, 61273216, and 61272111, the China Postdoctoral Science Foundation （CPSF） under Grant No. 2015M582272, the Natural Science Foundation of Hubei Province of China under Grant No. 2016CFB158, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2042016kf0033.
文摘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）.
