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Token-based plagiarism detection for metamodels

Sağlam, Timur ORCID iD icon 1; Hahner, Sebastian ORCID iD icon 1; Wittler, Jan Willem 1; Kühn, Thomas 1
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000153639
Veröffentlicht am 19.12.2022
DOI: 10.1145/3550356.3556508
Zitationen: 1
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 09.11.2022
Sprache Englisch
Identifikator ISBN: 978-1-4503-9467-3
KITopen-ID: 1000153639
HGF-Programm 46.23.03 (POF IV, LK 01) Engineering Security for Mobility Systems
Erschienen in MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion ProceedingsOctober 2022
Veranstaltung 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2022), Montreal, Kanada, 23.10.2022 – 28.10.2022
Verlag Association for Computing Machinery (ACM)
Seiten 138–141
Schlagwörter Plagiarism Detection, Token-based Plagiarism Detection, Metamodeling, Metamodel Similarity, Obfuscation Attacks, Education, JPlag
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