Token-Based Plagiarism Detection for Statecharts: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Jonas Strittmatter |email=uzxhf@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Timur Sağlam |termin=Institutsseminar/2023-04-28…“) |
Keine Bearbeitungszusammenfassung |
||
Zeile 6: | Zeile 6: | ||
|termin=Institutsseminar/2023-04-28 | |termin=Institutsseminar/2023-04-28 | ||
|vortragsmodus=in Präsenz | |vortragsmodus=in Präsenz | ||
|kurzfassung= | |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. | |||
}} | }} |
Version vom 17. April 2023, 12:48 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. |