mlsanityData to 14 Oct 2026

Understanding the Factors that Impact the Popularity of GitHub Repositories

arXiv 1606.04984 ↗First posted 15 Jun 2016cs.SE cs.SIcited by 27

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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.

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

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Citing papers per year: 2021: 3; 2022: 9; 2023: 3; 2024: 3; 2025: 2; 2026: 7.

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  1. 2023Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMscited by 45
  2. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  3. 2025From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agentscited by 14
  4. 2022Code Recommendation for Open Source Software Developerscited by 13
  5. 2024PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Softwarecited by 9
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  7. 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requestscited by 6
  8. 2021Manas: Mining Software Repositories to Assist AutoMLcited by 5
  9. 2025Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarizationcited by 4
  10. 2022GitRanking: A Ranking of GitHub Topics for Software Classification using Active Samplingcited by 3
  11. 2026Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answeringcited by 3
  12. 2022Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystemscited by 2
15 more
  1. 2022Automatically Categorising GitHub Repositories by Application Domaincited by 2
  2. 2022An Empirical Study of Library Usage and Dependency in Deep Learning Frameworkscited by 2
  3. 2022Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reportscited by 1
  4. 2022Antipatterns in Software Classification Taxonomiescited by 1
  5. 2024Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Qualitycited by 1
  6. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Reportcited by 1
  7. 2022Is Surprisal in Issue Trackers Actionable?cited by 0
  8. 2022Triadic Temporal Exponential Random Graph Models (TTERGM)cited by 0
  9. 2023What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflowcited by 0
  10. 2023MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activitiescited by 0
  11. 2024Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projectscited by 0
  12. 2026Deja 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 Benchmarkcited by 0
  13. 2026The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systemscited by 0
  14. 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
  15. 2026A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysiscited by 0