Predicting Popularity of Open Source Projects Using Recurrent Neural Networks

Sefa Eren Sahin*, Kubilay Karpat, Ayse Tosun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationOpen Source Systems - 15th IFIP WG 2.13 International Conference, OSS 2019, Proceedings
EditorsFrancis Bordeleau, Alberto Sillitti, Paulo Meirelles, Valentina Lenarduzzi
PublisherSpringer New York LLC
Number of pages11
ISBN (Print)9783030208820
Publication statusPublished - 2019
Event15th International Conference on Open Source Systems, OSS 2019 - Montreal, Canada
Duration: 26 May 201927 May 2019

Publication series

NameIFIP Advances in Information and Communication Technology
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X


Conference15th International Conference on Open Source Systems, OSS 2019

Bibliographical note

Publisher Copyright:
© IFIP International Federation for Information Processing 2019.


Acknowledgments. 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.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu5170048
Istanbul Teknik ÜniversitesiMGA-2017-40712


    • Open source projects
    • Predicting stars
    • Recurrent Neural Networks


    Dive into the research topics of 'Predicting Popularity of Open Source Projects Using Recurrent Neural Networks'. Together they form a unique fingerprint.

    Cite this