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The Measurement of the Software Ecosystem’s Productivity with GitHub

Zhifang Liao1, Yiqi Zhao1, Shengzong Liu2, Yan Zhang3, Limin Liu1,*, Jun Long1

1 School of Computer Science and Engineering, Central South University, Changsha, 410075, China
2 School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410075, China
3 Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK

* Corresponding Author: Limin Liu. Email: email

Computer Systems Science and Engineering 2021, 36(1), 239-258. https://doi.org/10.32604/csse.2021.014144

Abstract

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.

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Cite This Article

Z. Liao, Y. Zhao, S. Liu, Y. Zhang, L. Liu et al., "The measurement of the software ecosystem’s productivity with github," Computer Systems Science and Engineering, vol. 36, no.1, pp. 239–258, 2021. https://doi.org/10.32604/csse.2021.014144

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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