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Vorträge
| Vortragende(r)
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Hannes Greule
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| Titel
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Evidence-based Token Abstraction for Software Plagiarism Detection
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| Vortragstyp
|
Bachelorarbeit
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| Betreuer(in)
|
Timur Sağlam
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| Vortragssprache
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| Vortragsmodus
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in Präsenz
|
| Kurzfassung
|
Programming assignments for students are target of plagiarism. Especially for graded assignments, instructors want to detect plagiarism among the students. For larger courses, however, manual inspection of all submissions is a resourceful task. For this purpose, there are numerous tools that can help detect plagiarism in submissions. Many well-known plagiarism detection tools are token-based detectors. In an abstraction step, they map source code to a list of tokens, and such lists are then compared with each other. While there is much research in the area of comparison algorithms, the mapping is often only considered superficially. In this work, we conduct two experiments that address the issue of token abstraction. For that, we design different token abstractions and explain their differences. We then evaluate these abstractions using multiple datasets. We show that different abstractions have pros and cons, and that a higher abstraction level does not necessarily perform better. These findings are useful when adding support for new programming languages and for improving existing plagiarism detection tools. Furthermore, the results can be helpful to choose abstractions tailored to specific requirements.
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| Vortragende(r)
|
Jonas Strittmatter
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| Titel
|
Token-Based Plagiarism Detection for Statecharts
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
|
Timur Sağlam
|
| Vortragssprache
|
|
| Vortragsmodus
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in Präsenz
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| 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.
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