Source code plagiarism detection tools have been readily available for use in academia and industry for a long time. While there are many motivations for the usage of these tools in Computer Science education, they are often not implemented on an institutional level. This thesis will describe an efficient process of using source code plagiarism detec-tion tools in academia today. A literature review and a survey will be used to describe the process of finding source code plagiarism. A survey will collect some attitudes of instructors that affect the qualitative concept of efficiency. An interview with respondents using a tool integrated in a learning management system will collect more attitudes regarding efficiency. The administration of source code plagiarism detection tools will be analyzed. The process can be divided into collection, analysis, confirmation, and investigation. Some stages can be fully or partly automated with human intervention. Without integration into a learning management system, the efficiency already gained in the collection stage by using a learning management system will be lost. Some respondents were satisfied with the process of finding source code plagiarism without using any tool. The motivations of using plagiarism detection tools differ among respondents, but they shared positive attitudes of using a tool and using it more often. An efficient process is a university with a plagiarism policy that seeks plagiarism automatically without prior suspicion, using a tool integrated in a learning management system or a repository, with similarity reports of good usability.

This semestral thesis presents definition of plagiarism and focuses primarily on solving this problem in academic world. The main topic is the detection of plagiarism. It is discussed the various steps of the detection process and special attention is given to plagiarism detection of program codes. The work mentions question of the reliability of detection tools and divides the plagiarism detection methods into basic groups. One chapter is devoted metrics for comparing files. Mentioned are two tools available to detect plagiarism. In the last chapter is analyzed own draft program for plagiarism detection of program codes. The detector was applied to a database of student’s works, and the results were plotted.

Source code plagiarism is an ongoing problem in programming courses at higher academic institutions. For this reason, different automated source code plagiarism detection tools have been developed. However, they require several manual steps before the submissions can be compared. Linnaeus University uses GitLab to handle their students’ code-related assignments but lacks an integrated workflow for checking submissions against plagiarism. Instead, Linnaeus University’s plagiarism-checking process is done manually, which is a challenging and time-consuming process. This thesis is a case study on Linnaeus University, focusing on integrating one of the plagiarism detection tools with GitLab using Continuous integration pipelines. The objectives have been to collect students’ submissions, communicate with the plagiarism tool, and visually present the results within GitLab. The prototype has been evaluated by a set of manually created submissions with different levels of plagiarism to ensure that the detection tool differentiates plagiarized and non-plagiarized submissions. Teachers at Linnaeus University have tested the workflow and reasoned whether the prototype fulfills their requirements.