Token-based plagiarism detection for metamodels

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Abstract

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.

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APA

Saǧlam, T., Hahner, S., Wittler, J. W., & Kühn, T. (2022). Token-based plagiarism detection for metamodels. In Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings (pp. 138–141). Association for Computing Machinery, Inc. https://doi.org/10.1145/3550356.3556508

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