mlsanityData to 7 Oct 2026

Sampling Projects in GitHub for MSR Studies

arXiv 2103.04682 ↗First posted 8 Mar 2021cs.SEcited by 20

Not on the map. Why?

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 5; 2026: 11.

20 papers.

Sort:

By area:

  1. 2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
  2. 2026Agentic Much? Adoption of Coding Agents on GitHubcited by 31
  3. 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
  4. 2026Harness Engineering for Agentic AI Coding Tools: An Exploratory Studycited by 10
  5. 2023Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingcited by 8
  6. 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systemscited by 6
  7. 2025GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Gitcited by 5
  8. 2026Are Coding Agents Generating Over-Mocked Tests? An Empirical Studycited by 5
  9. 2026From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skillscited by 5
  10. 2025The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completioncited by 3
  11. 2026Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integritycited by 3
  12. 2026A Dataset of Agentic AI Coding Tool Configurationscited by 3
8 more
  1. 2025Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducementcited by 2
  2. 2023Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?cited by 1
  3. 2026AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptationcited by 1
  4. 2025Exploringand Unleashing the Power of Large Language Models in CI/CD Configuration Translationcited by 0
  5. 2026Multi-Agent Coordinated Rename Refactoringcited by 0
  6. 2026The Invisible Hand of AI Libraries Shaping Open Source Projects and Communitiescited by 0
  7. 2026Agentic Very Much! Adoption of Coding Agent in New GitHub Projectscited by 0
  8. 2026AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agentcited by 0