# Predicting the Popularity of GitHub Repositories

arXiv: https://arxiv.org/abs/1607.04342 · HTML: https://mlsanity.com/paper/1607.04342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2016.

## Built on by (most cited first, 6 of 6)

- [The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot](https://mlsanity.com/paper/2410.02091/) (2024)
- [AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software](https://mlsanity.com/paper/2512.18567/) (2025)
- [Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?](https://mlsanity.com/paper/2209.11946/) (2022)
- [Reputation Bootstrapping for Composite Services using CP-nets](https://mlsanity.com/paper/2105.15135/) (2021)
- [What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow](https://mlsanity.com/paper/2304.12857/) (2023)
- [A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis](https://mlsanity.com/paper/2609.07586/) (2026)

## Abstract (arXiv)

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).