# Understanding the Factors that Impact the Popularity of GitHub Repositories

arXiv: https://arxiv.org/abs/1606.04984 · HTML: https://mlsanity.com/paper/1606.04984/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2016.

## Built on by (most cited first, 27 of 27)

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- [On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests](https://mlsanity.com/paper/2110.15447/) (2021)
- [Manas: Mining Software Repositories to Assist AutoML](https://mlsanity.com/paper/2112.03395/) (2021)
- [Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarization](https://mlsanity.com/paper/2503.10876/) (2025)
- [GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling](https://mlsanity.com/paper/2205.09379/) (2022)
- [Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering](https://mlsanity.com/paper/2603.26567/) (2026)
- [Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems](https://mlsanity.com/paper/2201.07201/) (2022)
- [Automatically Categorising GitHub Repositories by Application Domain](https://mlsanity.com/paper/2208.00269/) (2022)
- [An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks](https://mlsanity.com/paper/2211.15733/) (2022)
- [Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports](https://mlsanity.com/paper/2202.06149/) (2022)
- [Antipatterns in Software Classification Taxonomies](https://mlsanity.com/paper/2204.08880/) (2022)
- [Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality](https://mlsanity.com/paper/2407.05466/) (2024)
- [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026)
- [Is Surprisal in Issue Trackers Actionable?](https://mlsanity.com/paper/2204.07363/) (2022)
- [Triadic Temporal Exponential Random Graph Models (TTERGM)](https://mlsanity.com/paper/2211.16229/) (2022)
- [What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow](https://mlsanity.com/paper/2304.12857/) (2023)
- [MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activities](https://mlsanity.com/paper/2308.16464/) (2023)
- [Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects](https://mlsanity.com/paper/2402.16546/) (2024)
- [Deja Vu at Scale: Paraphrase-Robust Detection of Duplicate Gherkin Steps in Behaviour-Driven Software Testing with Sentence-Transformer Embeddings and a 1.1M-Step Open Benchmark](https://mlsanity.com/paper/2604.20462/) (2026)
- [The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems](https://mlsanity.com/paper/2606.30524/) (2026)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)
- [A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis](https://mlsanity.com/paper/2609.07586/) (2026)

## Abstract (arXiv)

Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which can help them on building and evolving systems in a competitive software market.