Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

Abstract

In pragmatic courses, graduate students are required to submit programming assignments, which have been susceptible to various forms of plagiarism. Detecting counterfeited code in an academic setting is of paramount importance, given the prevalence of publications and papers. Plagiarism, defined as the unauthorized replication of written work without proper acknowledgment, has become a critical concern with the advent of information and communication technology (ICT) and the widespread availability of scholarly publications online. However, the extensive use of freeware text editors has posed challenges in detecting source code plagiarism. Numerous studies have investigated algorithms for revealing different types of plagiarism and detecting source code plagiarism. In this research, we propose an innovative strategy that combines TF-IDF (Term Frequency-Inverse Document Frequency) modifications with K-means clustering, achieving a remarkable precision rate of 99.2%. Additionally, we explore the hierarchical clustering method, which estimates an even higher precision rate of 99.5% compared to previous techniques. To implement our approach, we utilize the Python programming language along with relevant libraries, providing a robust and efficient system for source code plagiarism detection in student assignment submissions.