Discovering and exploring cases of educational source code plagiarism with Dolos
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Abstract (arXiv)
Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty. This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code. In this new version, the primary focus has been on enhancing the user experience. Educators can now run the entire plagiarism detection pipeline from a new web app in their browser, eliminating the need for any installation or configuration. Completely redesigned analytics dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism and how widespread plagiarism is within the collection. The dashboards support hierarchically structured navigation to facilitate zooming in and out of suspect cases. Clusters are an essential new component of the dashboard design, reflecting the observation that plagiarism can occur among larger groups of students. To meet various user needs, the Dolos software stack for source code plagiarism detections now includes a web interface, a JSON application programming interface (API), a command line interface (CLI), a JavaScript library and a preconfigured Docker container. Clear documentation and a free-to-use instance of the web app can be found at https://dolos.ugent.be. The source code is also available on GitHub.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2024.
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Citing papers per year: 2024: 1; 2025: 5.
6 papers.
- 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 429
- 2025Measuring General Intelligence with Generated Gamescited by 21
- 2025VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contaminationcited by 12
- 2025VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Codingcited by 10
- 2025LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?cited by 3
- 2025Bin2Vec: Interpretable and Auditable Multi-View Binary Analysis for Code Plagiarism Detectioncited by 0
- 2025Bin2Vec: Interpretable and Auditable Multi-View Binary Analysis for Code Plagiarism Detection1 Dec 2025
- 2025LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?10 Oct 2025
- 2025Measuring General Intelligence with Generated Games12 May 2025
- 2025VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination17 Mar 2025
- 2025VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding17 Mar 2025
- 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering9 Oct 2024