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Vorträge
| Vortragende(r)
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Gabriel Gehrig
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| Titel
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Enabling the Collaborative Collection of Uncertainty Sources Regarding Confidentiality
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
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Sebastian Hahner
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| Vortragsmodus
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in Präsenz
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| Kurzfassung
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With digitalization in progress, the amount of sensitive data stored in software systems is increasing. However, the confidentiality of this data can often not be guaranteed, as uncertainties with an impact on confidentiality exist, especially in the early stages of software development. As the consideration of uncertainties regarding confidentiality is still novel, there is a lack of awareness of the topic among software architects. Additionally, the existing knowledge is scattered among researchers and institutions, making it challenging to comprehend and utilize for software architects. Current research on uncertainties regarding confidentiality has focused on analyzing software systems to assess the possibilities of confidentiality violations, as well as the development of methods to classify uncertainties. However, these approaches are limited to the researchers’ observed uncertainties, limiting the generalizability of classification systems, the validity of analysis results, and the development of mitigation strategies. This thesis presents an approach to enable the collection and management of knowledge on uncertainties regarding confidentiality, enabling software architects to comprehend better and identify uncertainties regarding confidentiality. Furthermore, the proposed approach strives to enable collaboration between researchers and practitioners to manage the effort to collect the knowledge and maintain it. To validate this approach, a prototype was developed and evaluated with a user study of 17 participants from software engineering, including 7 students, 5 researchers, and 5 practitioners. Results show that the approach can support software architects in identifying and describing uncertainties regarding confidentiality, even with limited prior knowledge, as they could identify and describe uncertainties correctly in a close-to-real-world scenario in 94.4% of the cases.
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| Vortragende(r)
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Niklas Heneka
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| Titel
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Software Plagiarism Detection on Intermediate Representation
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
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Timur Sağlam
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| Vortragsmodus
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in Präsenz
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| Kurzfassung
|
Source code plagiarism is a widespread problem in computer science education. To counteract this, software plagiarism detectors can help identify plagiarized code. Most state-of-the-art plagiarism detectors are token-based. It is common to design and implement a new dedicated language module to support a new programming language. This process can be time-consuming, furthermore, it is unclear whether it is even necessary. In this thesis, we evaluate the necessity of dedicated language modules for Java and C/C++ and derive conclusions for designing new ones. To achieve this, we create a language module for the intermediate representation of LLVM. For the evaluation, we compare it to two existing dedicated language modules in JPlag. While our results show that dedicated language modules are better for plagiarism detection, language modules for intermediate representations show better resilience to obfuscation attacks.
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