Plagiarism Detection Tool Based on Programming Activity Logs

In academia, plagiarism is a critical concern, and educators require effective tools to identify and prevent it. Presently, many plagiarism detection tools rely on source code comparison, which may not effectively counter the obfuscation techniques employed by students. This article presents an innovative solution to identifying potential plagiarism in programming assignments through the analysis of logs containing information on user actions during the programming process. The created tool adopts a history-based approach to plagiarism detection, which helps to counteract certain forms of obfuscation students use to conceal their plagiarism. The plagiarism detection tool analyses logs based on user-specific criteria such as run count, total time spent working, log file size, and pasted text ratio. The tool also compares log files for detection of duplicate files, identical texts pasted in different log files, source code pasted in different log files, and source code similarity. The solution also allows the user to specify the values for each analyzed metric for plagiarism detection. The effectiveness of the tool is demonstrated through experimental evaluations, enabling to identify cases of plagiarism that could not be detected with other available tools. The findings suggest that the tool can be an efficient and effective means for educators to identify plagiarism in programming assignments.