Token-Based Plagiarism Detection for Statecharts: Unterschied zwischen den Versionen
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|kurzfassung=In the field of software engineering, existing plagiarism detection systems have primarily | |kurzfassung=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. | ||
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. | |||
}} | }} | ||
Aktuelle Version vom 19. April 2023, 13:47 Uhr
| Vortragende(r) | Jonas Strittmatter | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Timur Sağlam | |
| Termin | Fr 28. April 2023 | |
| Vortragssprache | ||
| Vortragsmodus | in Präsenz | |
| Kurzfassung | 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. | |