Abstract
The importance of social communities around open-source software projects has been recognized. Despite that a lot of relevant research focusing on this topic, understanding the structures and dynamics of communities around open-source software projects remains a tedious and challenging task. As a result, an easily accessible and useful application that enables project developers to gain awareness of the status and development of the project communities is desirable. In this paper, we present MyCommunity, a web-based online application system to automatically extract communication-based community structures from social coding platforms such as GitHub. Based on the detected community structures, the system analyzes and visualizes the community evolution history of a project with a set of semantic-rich events, and quantify the strength of community evolution with respect to different events with a series of indexes. Built-in support to quantitative analysis and machine learning tasks based on the quantitative evolutionary events are provided. We demonstrate the usefulness of the system by presenting its ability in predicting project success or failure with the community evolution features. The results suggest the system achieves a prediction accuracy of 88.5% with commonly available machine learning models.
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References
Antwerp, M.V., Madey, G.: The importance of social network structure in the open source software developer community. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS) (2010)
Begel, A., Khoo, Y.P., Zimmermann, T.: Codebook: discovering and exploiting relationships in software repositories. In: ACM/IEEE International Conference on Software Engineering (2010)
Catolino, G., Palomba, F., Tamburri, D.A., Serebrenik, A., Ferrucci, F.: Gender diversity and women in software teams: how do they affect community smells? In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS) (2019)
Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 1–37 (2017)
Christ, M., Kempa-Liehr, A.W., Feindt, M.: Distributed and parallel time series feature extraction for industrial big data applications (2016)
Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - a Python package). Neurocomputing 307, 72–77 (2018)
Coelho, J., Valente, M.T.: Why modern open source projects fail. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 186–196 (2017)
Ducheneaut, N.: Socialization in an open source software community: a socio-technical analysis. Comput. Support. Coop. Work 14(4), 323–368 (2005)
Hannemann, A., Klamma, R.: Community dynamics in open source software projects: aging and social reshaping. In: Petrinja, E., Succi, G., El Ioini, N., Sillitti, A. (eds.) OSS 2013. IAICT, vol. 404, pp. 80–96. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38928-3_6
Hong, Q., Kim, S., Cheung, S.C., Bird, C.: Understanding a developer social network and its evolution. In: 2011 27th IEEE International Conference on Software Maintenance (ICSM), pp. 323–332. IEEE (2011)
Jaruchotrattanasakul, T., Yang, X., Makihara, E., Fujiwara, K., Iida, H.: Open source resume (OSR): a visualization tool for presenting OSS biographies of developers. In: 2016 7th International Workshop on Empirical Software Engineering in Practice (IWESEP), pp. 57–62 (2016). https://doi.org/10.1109/IWESEP.2016.17
Le, Q., Panchal, J.H.: Analysis of the interdependent co-evolution of product structures and community structures using dependency modelling techniques. J. Eng. Des. 23(10–11), 807–828 (2012)
Lin, Y.R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.L.: Blog community discovery and evolution based on mutual awareness expansion. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI 2007), pp. 48–56. IEEE (2007)
Newman, M.E.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004)
Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)
Wang, L., Li, Y., Zhang, J., Tao, X.: Quantitative analysis of community evolution in developer social networks around open source software projects. arXiv preprint arXiv:2205.09935 (2022)
Zhou, S., Stanciulescu, S., Leßenich, O., Xiong, Y., Wasowski, A., Kästner, C.: Identifying features in forks. In: 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), pp. 105–116. IEEE (2018)
Acknowledgement
This work is supported by the National Key R &D Program of China under Grant No. 2018AAA0102302, the NSFC under Grant No. 62172203, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhang, J., Wang, L., Zheng, Z., Tao, X. (2022). Social Community Evolution Analysis and Visualization in Open Source Software Projects. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_4
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DOI: https://doi.org/10.1007/978-3-031-20891-1_4
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