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In the field of software engineering, existing plagiarism detection systems have primarily focused on detecting cases of plagiarism in code. However, other artefacts such as models also play a crucial role in the development process. Statecharts, in particular, are used to model the behavior of a system. This thesis investigates the applicability and challenges of applying token-based plagiarism detection systems to statecharts. We extend the plagiarism detector JPlag to support detecting cases of plagiarism in statecharts. Our approach is evaluated using a dataset of student assignments from a modeling course, where we generate plagiarized statecharts by adopting common obfuscation attacks. We study the effects of the token-extraction strategy, sorting techniques and the minimum token match parameter. The results suggest that an approach tailored to the specific kind of model, such as statecharts, works better than a generic solution for models.
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