Discovering plagiarism in introductory programming courses through the application of multiple methods

Abstract: Plagiarism is a common problem in the academic world. Multiple studies show that over 30 % of students attending introductory university programming courses plagiarise their code at least once. However, as opposed to plagiarism of academic texts, plagiarised code is harder to discover as subtle changes can be made, for instance renaming variables. The field of code plagiarism is not uncharted and there are indeed a few suggested approaches. This thesis investigates if it is possible to combine multiple plagiarism detection techniques into a user-friendly tool intended for examiners of introductory programming courses. The tool, named DecPlag, not only checks for plagiarism between new submissions, but also keeps a database of previous course iterations so that foul play can be discovered between new students and older students as well. The resulting tool developed for the research turned out to become slow, taking multiple days to finish comparing the data of two course iterations. It also outputs a large number of false positives due to the structure of the research data. However, DecPlag did indeed find potential plagiarism cases as well.