Identifying Code Plagiarism on C# Assignments

Abstract

To maintain academic integrity, students involved in plagiarism should be identified and penalized. A number of similarity detectors have been developed for that purpose. However, only a few of them are dedicated to C# programming language although the language is often used in courses about application development. Existing C# detectors either are time-inefficient, do not work on incomplete code, or have data privacy concerns. This paper presents a C# similarity detector that can work with large and incomplete submissions offline. Our evaluation shows that the detector is effective in identifying suspected submissions and reporting similar GitHub projects. It is also time efficient as it can process 208 submissions with 9.3 MB C# code in 22 seconds.

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