short-paper

Token-based plagiarism detection for metamodels

Authors:

Sebastian Hahner,

Jan Willem Wittler,

Thomas KühnAuthors Info & Claims

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Pages 138 - 141

https://doi.org/10.1145/3550356.3556508

Published: 09 November 2022 Publication History

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.

References

[1]

Alex Aiken. 2022. MOSS Software Plagiarism Detector Website. Stanford University. http://theory.stanford.edu/~aiken/moss/ Accesed: 2022-07-20.

[2]

Önder Babur et al. 2019. Metamodel clone detection with SAMOS. COLA 51 (2019), 57--74.

[3]

Jess Bidgood and Jeremy B. Merrill. 2017. As computer coding classes swell, so does cheating. The New York Times (2017). https://www.nytimes.com/2017/05/29/us/computer-science-cheating.html Accessed: 2022-07-21.

[4]

Tracy Camp et al. 2017. Generation CS: The Growth of Computer Science. ACM Inroads 8, 2 (2017), 44--50.

Digital Library

[5]

Federico Ciccozzi et al. 2018. How Do We Teach Modelling and Model-Driven Engineering? A Survey. In MODELS-C. ACM, 122--129.

[6]

Georgina Cosma and Mike Joy. 2008. Towards a Definition of Source-Code Plagiarism. IEEE Transactions on Education 51, 2 (2008), 195--200.

Digital Library

[7]

Breanna Devore-McDonald and Emery D. Berger. 2020. Mossad: Defeating Software Plagiarism Detection. PACMPL 4 (2020), 1--28.

[8]

Mike Joy and Micheal Luck. 1999. Plagiarism in programming assignments. IEEE Transactions on Education 42, 2 (1999), 129--133.

Digital Library

[9]

Oscar Karnalim. 2016. Detecting source code plagiarism on introductory programming course assignments using a bytecode approach. In ICTS. IEEE, 63--68.

[10]

Salvador Martínez et al. 2020. Efficient plagiarism detection for software modeling assignments. Computer Science Education 30, 2 (jan 2020), 187--215.

[11]

William Murray. 2010. Cheating in Computer Science. Ubiquity 2010 (2010).

[12]

Matija Novak et al. 2019. Source-Code Similarity Detection and Detection Tools Used in Academia: A Systematic Review. TOCE 19, 3 (2019), 1--37.

[13]

K. J. Ottenstein. 1976. An Algorithmic Approach to the Detection and Prevention of Plagiarism. SIGCSE Bulletin 8, 4 (1976), 30--41.

Digital Library

[14]

Lutz Prechelt et al. 2002. Finding plagiarisms among a set of programs with J Plag. Journal of Universal Computer Science 8, 11 (2002).

[15]

Saul Schleimer et al. 2003. Winnowing: Local Algorithms for Document Fingerprinting. In SIGMOD. ACM, 76--85.

Digital Library

[16]

G. Shobha et al. 2021. Code Clone Detection---A Systematic Review. In IEMIS. Springer Nature Singapore, 645--655.

[17]

G Shobha et al. 2021. Comparison between Code Clone Detection and Model Clone Detection. In ICRITO. IEEE, 1--5.

[18]

David Steinberg et al. 2009. EMF: Eclipse Modeling Framework 2.0 (2nd ed.). Addison-Wesley Professional.

[19]

Matthew Stephan and James R. Cordy. 2013. A Survey of Model Comparison Approaches and Applications. In MODELSWARD. INSTICC, SciTePress, 265--277.

[20]

Harald Störrle. 2015. Effective and Efficient Model Clone Detection. Springer, 440--457.

[21]

Debora Weber-Wulff et al. 2012. Collusion detection system test report 2012. Hochschule für Technik und Wirtchaft, Berlin, Tech. Rep (2012). Technical Report.

[22]

Michael Wise. 1993. String Similarity via Greedy String Tiling and Running Karp-Rabin Matching. Basser Departement of Computer Science Report (01 1993).

[23]

Lisa Yan et al. 2018. TMOSS: Using Intermediate Assignment Work to Understand Excessive Collaboration in Large Classes. In SIGCSE. ACM, 110--115.

Cited By

Sağlam THahner SSchmid LBurger ERoychoudhury APaiva AAbreu RStorey M(2024)Obfuscation-Resilient Software Plagiarism Detection with JPlagProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3643074(264-265)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3643074
Sağlam THahner SSchmid LBurger ERoychoudhury APaiva AAbreu RStorey MGama KSiegmund J(2024)Automated Detection of AI-Obfuscated Plagiarism in Modeling AssignmentsProceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3639474.3640084(297-308)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639474.3640084
Sağlam TSchmid LHahner SBurger E(2023)How Students Plagiarize Modeling Assignments2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C59198.2023.00032(98-101)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS-C59198.2023.00032

Index Terms

Token-based plagiarism detection for metamodels

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Published In

cover image ACM Conferences

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

October 2022

1003 pages

ISBN:9781450394673

DOI:10.1145/3550356

Conference Chairs:
Thomas Kühn
Karlsruhe Institute of Technology, Germany
,
Vasco Sousa
University of Montréal, Canada

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSOFT: ACM Special Interest Group on Software Engineering

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Univ. of Montreal: University of Montreal
IEEE CS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 November 2022

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Short-paper

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Bundesministerium für Wirtschaft und Klimaschutz
Helmholtz-Gemeinschaft Deutscher Forschungszentren

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MODELS '22

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SIGSOFT

MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems

October 23 - 28, 2022

Quebec, Montreal, Canada

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Overall Acceptance Rate 118 of 382 submissions, 31%

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Cited By

Sağlam THahner SSchmid LBurger ERoychoudhury APaiva AAbreu RStorey M(2024)Obfuscation-Resilient Software Plagiarism Detection with JPlagProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3643074(264-265)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3643074
Sağlam THahner SSchmid LBurger ERoychoudhury APaiva AAbreu RStorey MGama KSiegmund J(2024)Automated Detection of AI-Obfuscated Plagiarism in Modeling AssignmentsProceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3639474.3640084(297-308)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639474.3640084
Sağlam TSchmid LHahner SBurger E(2023)How Students Plagiarize Modeling Assignments2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C59198.2023.00032(98-101)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS-C59198.2023.00032

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