What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform
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Abstract (arXiv)
Besides a git-based version control system, GitHub integrates several social coding features. Particularly, GitHub users can star a repository, presumably to manifest interest or satisfaction with an open source project. However, the real and practical meaning of starring a project was never the subject of an in-depth and well-founded empirical investigation. Therefore, we provide in this paper a throughout study on the meaning, characteristics, and dynamic growth of GitHub stars. First, by surveying 791 developers, we report that three out of four developers consider the number of stars before using or contributing to a GitHub project. Then, we report a quantitative analysis on the characteristics of the top-5,000 most starred GitHub repositories. We propose four patterns to describe stars growth, which are derived after clustering the time series representing the number of stars of the studied repositories; we also reveal the perception of 115 developers about these growth patterns. To conclude, we provide a list of recommendations to open source project managers (e.g., on the importance of social media promotion) and to GitHub users and Software Engineering researchers (e.g., on the risks faced when selecting projects by GitHub stars).
Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 5; 2023: 5; 2024: 5; 2025: 1; 2026: 7.
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- 2022Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?cited by 1
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- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
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- 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report12 Aug 2026
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- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests23 Jul 2026
- 2026ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems19 Jun 2026
- 2026Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures3 Apr 2026
- 2026The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities5 Jan 2026
- 2025Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects12 Nov 2025
- 2024Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering5 Dec 2024
- 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systems12 Aug 2024
- 2024XMainframe: A Large Language Model for Mainframe Modernization5 Aug 2024
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards4 Jul 2024
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- 2024An Empirical Study of Challenges in Machine Learning Asset Management25 Feb 2024
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requests13 Nov 2023
- 2023A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME3 May 2023
- 2023Characterizing the contribution of dependent features in XAI methods4 Apr 2023
- 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision13 Mar 2023
- 2023Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliability5 Mar 2023
- 2022An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks28 Nov 2022
- 2022Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?24 Sep 2022
- 2022Cross-Domain Evaluation of a Deep Learning-Based Type Inference System19 Aug 2022
- 2022GitRank: A Framework to Rank GitHub Repositories4 May 2022
- 2022Towards a Change Taxonomy for Machine Learning Systems21 Mar 2022
- 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests28 Oct 2021
- 2021Content-Based Textual File Type Detection at Scale21 Jan 2021
- 2026Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architecturescited by 15
- 2025Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projectscited by 13
- 2026Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applicationscited by 1
- 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Reportcited by 1
- 2026ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystemscited by 0
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
- 2026A Two-Dimensional Study of the Model Context Protocol: Publication and Adoptioncited by 0
- 2026A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption13 Sep 2026
- 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report12 Aug 2026
- 2026Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applications9 Aug 2026
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests23 Jul 2026
- 2026ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems19 Jun 2026
- 2026Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures3 Apr 2026
- 2025Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects12 Nov 2025
- 2023A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIMEcited by 42
- 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Visioncited by 11
- 2021On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requestscited by 6
- 2022Towards a Change Taxonomy for Machine Learning Systemscited by 6
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboardscited by 6
- 2024XMainframe: A Large Language Model for Mainframe Modernizationcited by 6
- 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systemscited by 6
- 2023Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliabilitycited by 4
- 2023Characterizing the contribution of dependent features in XAI methodscited by 4
- 2024An Empirical Study of Challenges in Machine Learning Asset Managementcited by 4
- 2022GitRank: A Framework to Rank GitHub Repositoriescited by 3
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requestscited by 3
12 most cited of 18 All 18 (JSON)
- 2026The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities5 Jan 2026
- 2024Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering5 Dec 2024
- 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systems12 Aug 2024
- 2024XMainframe: A Large Language Model for Mainframe Modernization5 Aug 2024
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards4 Jul 2024
- 2024An Empirical Study of Challenges in Machine Learning Asset Management25 Feb 2024
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requests13 Nov 2023
- 2023A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME3 May 2023
- 2023Characterizing the contribution of dependent features in XAI methods4 Apr 2023
- 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision13 Mar 2023
- 2023Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliability5 Mar 2023
- 2022An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks28 Nov 2022
12 newest of 18 All 18 (JSON)