# Sampling Projects in GitHub for MSR Studies

arXiv: https://arxiv.org/abs/2103.04682 · HTML: https://mlsanity.com/paper/2103.04682/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 21 of 21)

- [EnvBench: A Benchmark for Automated Environment Setup](https://mlsanity.com/paper/2503.14443/) (2025)
- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [From Commit Message Generation to History-Aware Commit Message Completion](https://mlsanity.com/paper/2308.07655/) (2023)
- [Harness Engineering for Agentic AI Coding Tools: An Exploratory Study](https://mlsanity.com/paper/2602.14690/) (2026)
- [Automating Code-Related Tasks Through Transformers: The Impact of Pre-training](https://mlsanity.com/paper/2302.04048/) (2023)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (2026)
- [From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills](https://mlsanity.com/paper/2607.01456/) (2026)
- [GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git](https://mlsanity.com/paper/2505.22583/) (2025)
- [The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion](https://mlsanity.com/paper/2508.16131/) (2025)
- [Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity](https://mlsanity.com/paper/2602.08816/) (2026)
- [A Dataset of Agentic AI Coding Tool Configurations](https://mlsanity.com/paper/2605.08435/) (2026)
- [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025)
- [Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?](https://mlsanity.com/paper/2308.08943/) (2023)
- [AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation](https://mlsanity.com/paper/2601.04540/) (2026)
- [Exploringand Unleashing the Power of Large Language Models in CI/CD Configuration Translation](https://mlsanity.com/paper/2511.01316/) (2025)
- [Multi-Agent Coordinated Rename Refactoring](https://mlsanity.com/paper/2601.00482/) (2026)
- [The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities](https://mlsanity.com/paper/2601.01944/) (2026)
- [Agentic Very Much! Adoption of Coding Agent in New GitHub Projects](https://mlsanity.com/paper/2606.07448/) (2026)
- [AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent](https://mlsanity.com/paper/2608.29204/) (2026)
- [Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?](https://mlsanity.com/paper/2610.04716/) (2026)

## Abstract (arXiv)

Almost every Mining Software Repositories (MSR) study requires, as first step, the selection of the subject software repositories. These repositories are usually collected from hosting services like GitHub using specific selection criteria dictated by the study goal. For example, a study related to licensing might be interested in selecting projects explicitly declaring a license. Once the selection criteria have been defined, utilities such as the GitHub APIs can be used to "query" the hosting service. However, researchers have to deal with usage limitations imposed by these APIs and a lack of required information. For example, the GitHub search APIs allow 30 requests per minute and, when searching repositories, only provide limited information (e.g., the number of commits in a repository is not included). To support researchers in sampling projects from GitHub, we present GHS (GitHub Search), a dataset containing 25 characteristics (e.g., number of commits, license, etc.) of 735,669 repositories written in 10 programming languages. The set of characteristics has been derived by looking for frequently used project selection criteria in MSR studies and the dataset is continuously updated to (i) always provide fresh data about the existing projects, and (ii) increase the number of indexed projects. The GHS dataset can be queried through a web application we built that allows to set many combinations of selection criteria needed for a study and download the information of matching repositories: https://seart-ghs.si.usi.ch.