NDLI: Predicting the Popularity of GitHub Repositories

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Predicting the Popularity of GitHub Repositories

Content Provider	ACM Digital Library
Author	Borges, Hudson Valente, Marco Tulio Hora, Andre
Abstract	GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450347723
DOI	10.1145/2972958.2972966
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Prediction models Github Popularity Social coding Open source development
Content Type	Text
Resource Type	Article

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