  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).
