Predicting the Popularity of GitHub Repositories

TL;DR

This paper presents a method using multiple linear regression to predict GitHub repository popularity based on star counts, with high accuracy in both star number and ranking predictions, aiding project assessment.

Contribution

It introduces a large-scale analysis of star prediction models and recommends specific models for different repository growth trends, improving prediction accuracy.

Findings

Predictions become accurate after six months of data.

Models for slow-growing repositories are effective.

Strong correlation (rho > 0.95) in rank prediction.

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).