World of Code: Enabling a Research Workflow for Mining and Analyzing the Universe of Open Source VCS data
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Abstract (arXiv)
Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are the tens of millions of projects in the periphery interconnected through. technical dependencies, code sharing, or knowledge flow? To answer such questions we: a) create a very large and frequently updated collection of version control data in the entire FLOSS ecosystems named World of Code (WoC), that can completely cross-reference authors, projects, commits, blobs, dependencies, and history of the FLOSS ecosystems and b) provide capabilities to efficiently correct, augment, query, and analyze that data. Our current WoC implementation is capable of being updated on a monthly basis and contains over 18B Git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 3.
7 papers.
- 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
- 2025Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasetscited by 4
- 2022JEMMA: An Extensible Java Dataset for ML4Code Applicationscited by 2
- 2024DocuMint: Docstring Generation for Python using Small Language Modelscited by 2
- 2026Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Codecited by 1
- 2026Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineeringcited by 0
- 2026Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositoriescited by 0
- 2026Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories23 Jun 2026
- 2026Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code13 May 2026
- 2026Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering28 Jan 2026
- 2025Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets5 Jan 2025
- 2024DocuMint: Docstring Generation for Python using Small Language Models16 May 2024
- 2022JEMMA: An Extensible Java Dataset for ML4Code Applications18 Dec 2022
- 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software19 Jul 2021