Token-based plagiarism detection for metamodels
Timur Sağlam, Sebastian Hahner, Jan Willem Wittler, Thomas Kühn · 2022
Plagiarism is a widespread problem in computer science education. Manual inspection is impractical for large courses, and the risk of detection is thus low. Many plagiarism detectors are available for programming assignments. However, very few approaches are available for modeling assignments. To remedy this, we introduce token-based plagiarism detection for metamodels. To this end, we extend the widely-used software plagiarism detector JPlag. We evaluate our approach with real-world modeling assignments and generated plagiarisms based on obfuscation attack classes. The results show that our approach outperforms the state-of-the-art.