Predicting Popularity of Open Source Projects Using Recurrent Neural Networks

  • Sefa Eren SahinEmail author
  • Kubilay KarpatEmail author
  • Ayse TosunEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 556)


GitHub is the largest open source software development platform with millions of repositories on variety of topics. The number of stars received by a repository is often considered as a measure of its popularity. Predicting the number of stars of a repository has been associated with the number of forks, commits, followers, documentation size, and programming language in the literature. We extend prior studies in terms of input features and algorithm: We define six features from GitHub events corresponding to the development activities, and additional six features incorporating the influence of users (followers and contributors) on the popularity of projects into their development activities. We propose a time-series based forecast model using Recurrent Neural Networks to predict the number of stars received in consecutive k days. We assess the performance of our proposed model with varying k (1, 7, 14, 30 days) and with varying input features. Our analysis on five topmost starred repositories in data visualization area shows that the error rate ranges between 19.76 and 70.57 among the projects. The best performing models use either features from development activities only, or all metrics including all the features.


Open source projects Predicting stars Recurrent Neural Networks 



This research is supported in part by Scientific Research Projects Division of Istanbul Technical University with project number MGA-2017-40712 and Scientific and Technological Research Council of Turkey with project number 5170048.


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey

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