What's Inside a GitHub Repository? An Empirical Study on the Contents of 10K Projects

• URL: http://arxiv.org/abs/2605.16701v1
• Date: Fri, 15 May 2026 23:30:44 GMT
• Title: What's Inside a GitHub Repository? An Empirical Study on the Contents of 10K Projects
• Abstract summary: This paper presents an initial empirical analysis to better understand the contents of real-world GitHub.<n>We analyze the files, directories, and extensions present in 10,000 GitHub repositories, as well as their evolution over ten years.
• Score: 1.439946676159516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GitHub is the largest code hosting platform, with millions of repositories spanning multiple technologies. Despite this, little is known about the actual contents of GitHub's repositories in the wild. This paper presents an initial empirical analysis to better understand the contents of real-world GitHub repositories. We analyze the files, directories, and extensions present in 10,000 GitHub repositories, as well as their evolution over ten years. Our results show major changes in GitHub over the last decade: (1) the consolidation of README.md, .gitignore, and LICENSE as standard artifacts; (2) the rise of GitHub Actions as the dominant CI/CD platform; (3) the growth of configuration formats such as TOML, YAML, and JSON, alongside a decline in XML; (4) new trends, such as the growth of Dockerfile; and (5) emerging content related to LLMs and generative AI (e.g., AGENTS.md). Based on our findings, we discuss implications, including that open source is not only evolving organically but also increasingly guided by GitHub's standards, the rise and fall of technologies, and the potential support for mining software repository studies.

Related papers

• Beyond Compliance: A Large Scale Study on the Completeness and Consistency of the GitHub SBOMs [1.068768727414769]
  We conduct a large-scale analysis of 10,000 GitHub repositories across ten programming language ecosystems.<n>We find a lack of NTIA compliance in GitHub SBOMs, though core metadata is consistently present.<n>We also find that component version and license information availability is highly dependent on the programming ecosystem.
  arXiv  Detail & Related papers  (2026-07-06T02:44:52Z)
• LLM-based Content Classification Approach for GitHub Repositories by the README Files [2.212685917364911]
  Large Language Models (LLMs) have shown great performance in many text-based tasks.<n>In this study, an approach is developed to fine-tune LLMs for automatically classifying different sections of GitHub files.<n>This approach outperforms current state-of-the-art methods and has achieved an overall F1 score of 0.98.
  arXiv  Detail & Related papers  (2025-07-29T15:09:38Z)
• SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
  We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
  arXiv  Detail & Related papers  (2025-05-29T18:28:02Z)
• Six Million (Suspected) Fake Stars in GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware [52.84746696418136]
  We present a systematic, global, and longitudinal measurement study of fake stars in GitHub.<n>We build StarScout, a scalable tool able to detect anomalous starring behaviors across all GitHub metadata between 2019 and 2024.<n>Analyzing the data collected using StarScout, we find that: (1) fake-star-related activities have rapidly surged in 2024; (2) the accounts and repositories in fake star campaigns have highly trivial activity patterns; and the majority of fake stars are used to promote short-lived phishing malware repositories.
  arXiv  Detail & Related papers  (2024-12-18T03:03:58Z)
• Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
  This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.<n>Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.<n>In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories [83.5195424237358]
  Existing benchmarks are poorly aligned with real-world code repositories. We propose a new benchmark named DevEval, which has three advances. DevEval comprises 1,874 testing samples from 117 repositories, covering 10 popular domains.
  arXiv  Detail & Related papers  (2024-05-30T09:03:42Z)
• Analyzing the Accessibility of GitHub Repositories for PyPI and NPM Libraries [91.97201077607862]
  Industrial applications heavily rely on open-source software (OSS) libraries, which provide various benefits.<n>To monitor the activities of such communities, a comprehensive list of repositories for the libraries of an ecosystem must be accessible.<n>In this study, we analyze the accessibility of GitHub repositories for PyPI and NPM libraries.
  arXiv  Detail & Related papers  (2024-04-26T13:27:04Z)
• MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution [47.850418420195304]
  Large Language Models (LLMs) have shown promise in code generation but face difficulties in resolving GitHub issues. We propose a novel Multi-Agent framework for GitHub Issue reSolution, MAGIS, consisting of four agents customized for software evolution.
  arXiv  Detail & Related papers  (2024-03-26T17:57:57Z)
• StarCoder 2 and The Stack v2: The Next Generation [105.93298676368798]
  We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens. We thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
  arXiv  Detail & Related papers  (2024-02-29T13:53:35Z)
• Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub [79.31134731122462]
  We introduce OpenAct benchmark to evaluate the open-domain task-solving capability, built on human expert consultation and repositories in GitHub.<n>We present OpenAgent, a novel LLM-based agent system that can tackle evolving queries in open domains through autonomously integrating specialized tools from GitHub.
  arXiv  Detail & Related papers  (2023-12-28T15:47:30Z)
• How do Software Engineering Researchers Use GitHub? An Empirical Study of Artifacts & Impact [0.2209921757303168]
  We ask whether and how authors engage in social coding related to their research. Ten thousand papers in top SE research venues, hand-annotating their GitHub links, and studying 309 paper-related repositories. We find a wide distribution in popularity and impact, some strongly correlated with publication venue.
  arXiv  Detail & Related papers  (2023-10-02T18:56:33Z)
• GitRank: A Framework to Rank GitHub Repositories [0.0]
  Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems. In this hackathon, we utilize known code quality measures and GrimoireLab toolkit to implement a framework, named GitRank, to rank open-source repositories on three different criteria.
  arXiv  Detail & Related papers  (2022-05-04T23:42:30Z)
• The penumbra of open source: projects outside of centralized platforms are longer maintained, more academic and more collaborative [0.0]
  We develop a novel, extensive sample of public open source project repositories outside of centralized platforms. Our sample projects tend to have more collaborators, are maintained for longer periods, and tend to be more focused on academic and scientific problems.
  arXiv  Detail & Related papers  (2021-06-29T17:54:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.