KIT | KIT-Bibliothek | Impressum | Datenschutz

Criminal Minds: How First-Year CS Students Plagiarize Code

Maisch, Robin ORCID iD icon 1,2; Schmid, Larissa ORCID iD icon; Glassey, Richard; Fuchß, Dominik ORCID iD icon 1,2; Niehues, Nils ORCID iD icon 1,2; Liu, Haoyu ORCID iD icon 1,2; Koziolek, Anne ORCID iD icon 1,2
1 Karlsruher Institut für Technologie (KIT)
2 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)

Abstract:

While academic integrity is essential in higher education, plagiarism by students in programming courses is a prevalent challenge. Plagiarism detectors are widely used, yet it is unclear if they match the ways students actually obfuscate copied code. Existing evaluation datasets are limited, often private, or rely on synthetically generated plagiarism instances. In a collaboration between two universities, we conduct an empirical study that challenges students at both institutions to plagiarize given solutions and document their changes. Using this dataset, we analyze their success in evading detection, present common plagiarism strategies, and provide a reliable resource for future research on academic integrity. While only few submissions plagiarize successfully, our participants incorporate a large variety of creative manual and LLM-based strategies.


Originalveröffentlichung
DOI: 10.1145/3803437.3805789
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2026
Sprache Englisch
Identifikator ISBN: 979-8-4007-2636-1
KITopen-ID: 1000191756
HGF-Programm 46.23.01 (POF IV, LK 01) Methods for Engineering Secure Systems
Erschienen in 34th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE Companion ’26), July 5–9, 2026, Montreal, QC, Canada
Veranstaltung 34th ACM Joint European Software Engineering Conference and Symposium of the Foundations of Software Engineering (2026), Montréal, Kanada, 05.07.2026 – 09.07.2026
Verlag Association for Computing Machinery (ACM)
Vorab online veröffentlicht am 28.03.2026
Externe Relationen Supplement
Schlagwörter plagiarism detection, large language models, LLMs, software engineering education, SE education, computer science education
KIT – Die Universität in der Helmholtz-Gemeinschaft
KITopen Landing Page