# A Study of Potential Code Borrowing and License Violations in Java Projects on GitHub

arXiv: https://arxiv.org/abs/2002.05237 · HTML: https://mlsanity.com/paper/2002.05237/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning](https://mlsanity.com/paper/2110.12925/) (2021)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [Automated Program Repair: Emerging trends pose and expose problems for benchmarks](https://mlsanity.com/paper/2405.05455/) (2024)
- [Full Line Code Completion: Bringing AI to Desktop](https://mlsanity.com/paper/2405.08704/) (2024)
- [Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks](https://mlsanity.com/paper/2405.01559/) (2024)

## Abstract (arXiv)

With an ever-increasing amount of open source software, the popularity of services like GitHub that facilitate code reuse, and common misconceptions about the licensing of open source software, the problem of license violations in the code is getting more and more prominent. In this study, we compile an extensive corpus of popular Java projects from GitHub, search it for code clones and perform an original analysis of possible code borrowing and license violations on the level of code fragments. We chose Java as a language because of its popularity in industry, where the plagiarism problem is especially relevant because of possible legal action. We analyze and discuss distribution of 94 different discovered and manually evaluated licenses in files and projects, differences in the licensing of files, distribution of potential code borrowing between licenses, various types of possible license violations, most violated licenses, etc. Studying possible license violations in specific blocks of code, we have discovered that 29.6% of them might be involved in potential code borrowing and 9.4% of them could potentially violate original licenses.