Detecting Automatic Software Plagiarism via Token Sequence Normalization
Author:
Affiliation:
1. KASTEL - Institute of Information Security and Dependability, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
2. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Funder
Bundesministerium für Wirtschaft und Klimaschutz
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3597503.3639192
Reference54 articles.
1. Alex Aiken. 2022. MOSS Software Plagiarism Detector Website. Stanford University. http://theory.stanford.edu/~aiken/moss/ Accessed: 2024-01-12.
2. Sherlock N-overlap: Invasive Normalization and Overlap Coefficient for the Similarity Analysis Between Source Code
3. Source Code Plagiarism Detection in an Educational Context: A Literature Mapping
4. A Methodology for Collecting Valid Software Engineering Data
5. Stella Biderman and Edward Raff. 2022. Fooling MOSS Detection with Pretrained Language Models In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta GA USA) Mohammad Al Hasan and Li Xiong 0001 (Eds.). International Conference on Information and Knowledge Management 2933--2943. 10.1145/3511808.3557079
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