Abstract
Open source software (OSS) community relies on volunteers and developers contributions for its survival. However, only a few projects reach success and popularity in open source community. Then, it is important to know the success factors of OSS projects. In this paper, we have applied time series clustering on open source projects hosted on a social coding platform to understand the main effective attributes of an OSS project on its popularity trends. We have applied exploratory data analysis on each cluster to see the effect of projects’ performance and attributes on projects’ reputation inside the OSS community. Finally, we have applied machine learning techniques to predict the popularity trend of OSS projects. Having access to the social coding data expands our view on project popularity on both social and technical factors. Results of this empirical study can help project owners and members to manage and promote the project reputation.
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Bayati, S., Heidary, M. (2020). Time Series Analysis of Open Source Projects Popularity. In: Lang, K.R., et al. Smart Business: Technology and Data Enabled Innovative Business Models and Practices. WeB 2019. Lecture Notes in Business Information Processing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-030-67781-7_8
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