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- Freitag, 15. Oktober 2021, 14:00 Uhr, https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams
| Vortragende(r)
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Ian Winter
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| Titel
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Komposition von Trace Link Recovery Ansätzen
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
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Jan Keim
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| Kurzfassung
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Das Erstellen von Trace-Links die beispielsweise Dokumentation mit Entwurfsmodellen verknüpfen ist ein wertvoller Bestandteil der Softwareentwicklung. Da ein manuelles Herauslesen der Trace-Links oft nicht praktikabel ist, sollte dieser Prozess automatisiert werden. Es existieren schon viele verschiedene Ansätze der Trace-Link-Recovery, welche jedoch unterschiedliche Stärken und schwächen haben. In dieser Arbeit wird untersucht, ob die Stärken unterschiedlicher Recovery-Ansätze durch Komposition verknüpft werden können, um ggf. die Schwächen auszugleichen. Dazu habe ich mehrere einfache Kompositionen implementiert und deren Ergebnisse ausgewertet. Dazu wird Ausbeute, Präzision, F1 und F2 verschiedener Kompositionen in drei Fallstudien ermittelt und mit denen der Basis-Ansätze verglichen.
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- Freitag, 15. Oktober 2021, 14:00 Uhr, https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams
| Vortragende(r)
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Oliver Liu
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| Titel
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Design Space Evaluation for Confidentiality under Architectural Uncertainty
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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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| Kurzfassung
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In the early stages of developing a software architecture, many properties of the final system are yet unknown, or difficult to determine. There may be multiple viable architectures, but uncertainty about which architecture performs the best. Software architects can use Design Space Exploration to evaluate quality properties of architecture candidates to find the optimal solution.
Design Space Exploration can be a resource intensive process. An architecture candidate may feature certain properties which disqualify it from consideration as an optimal candidate, regardless of its quality metrics. An example for this would be confidentiality violations in data flows introduced by certain components or combinations of components in the architecture. If these properties can be identified early, quality evaluation can be skipped and the candidate discarded, saving resources.
Currently, analyses for identifying such properties are performed disjunct from the design space exploration process. Optimal candidates are determined first, and analyses are then applied to singular architecture candidates. Our approach augments the PerOpteryx design space exploration pipeline with an additional architecture candidate filter stage, which allows existing generic candidate analyses to be integrated into the DSE process. This enables automatic execution of analyses on architecture candidates during DSE, and early discarding of unwanted candidates before quality evaluation takes place.
We use our filter stage to perform data flow confidentiality analyses on architecture candidates, and further provide a set of example analyses that can be used with the filter. We evaluate our approach by running PerOpteryx on case studies with our filter enabled. Our results indicate that the filter stage works as expected, able to analyze architecture candidates and skip quality evaluation for unwanted candidates.
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- Freitag, 22. Oktober 2021, 14:00 Uhr, Hybrid: https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams und Gebäude 50.34, Seminarraum 348 (NUR für Vortragende)
- Freitag, 22. Oktober 2021, 14:00 Uhr, https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams
- Freitag, 29. Oktober 2021, 11:30 Uhr, https://conf.dfn.de/webapp/conference/979160755
| Vortragende(r)
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Klevia Ulqinaku
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| Titel
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Analysis and Visualization of Semantics from Massive Document Directories
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
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Edouard Fouché
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| Kurzfassung
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Research papers are commonly classified into categories, and we can see the existing contributions as a massive document directory, with sub-folders. However, research typically evolves at an extremely fast pace; consider for instance the field of computer science. It can be difficult to categorize individual research papers, or to understand how research communities relate to each other.
In this thesis we will analyze and visualize semantics from massive document directories. The results will be displayed using the arXiv corpus, which contains domain-specific (computer science) papers of the past thirty years. The analysis will illustrate and give insight about past trends of document directories and how their relationships evolve over time.
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