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Vorträge
Automated Mitigation of Confidentiality Violations in Software Architectures using Discrete Optimization
| Vortragende(r)
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Benjamin Arp
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| Vortragstyp
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Masterarbeit
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| Betreuer(in)
|
Nils Niehues
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| Vortragssprache
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Englisch
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| Vortragsmodus
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in Präsenz
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| Kurzfassung
|
Identifying confidentiality violations in software architectures at design time is a well-studied problem.
Fixing them automatically is not.
Frameworks such as STRIDE and LINDDUN use Data Flow Diagrams (DFDs) to expose threats at an early stage in the development lifecycle.
But once a violation is detected, software architects must still manually determine and apply the appropriate countermeasures, which is both a time-consuming and error-prone process.
This thesis addresses the repair side of the problem.
The central research question is which discrete optimisation method is best suited to automate this task.
To answer it without presupposing an outcome, we design a comparative survey of three candidate methods: Branch and Bound, Integer Linear Programming (ILP), and Evolutionary Algorithms.
These are evaluated against four criteria: optimality, runtime performance, extensibility, and reproducibility.
The survey establishes ILP as the only method satisfying all four, primarily because its declarative problem formulation separates constraint specification from solving.
This separation proves essential when mitigation strategies must be extended or customised.
Building on this result, we implement an ILP-based automated mitigation approach integrated into the ARCoViA framework.
The approach operates on DFDs that are annotated with label-based confidentiality constraints.
It then enumerates candidate mitigation strategies across the full space of label additions and deletions, node insertions and removals, as well as flow deletions.
These strategies, along with their mutual dependencies and contradictions, are encoded into a Boolean ILP problem.
The solver yields a minimal-cost repair, which is then applied to produce a repaired DFD automatically.
The main engineering challenge is generating a complete and correct set of candidate mitigations and encoding their dependencies and contradictions without omissions.
The prior SAT-based approach, which this work extends, is limited to purely additive label changes and struggles to express richer constraint structures in CNF form.
The present approach removes both restrictions.
We evaluate it against four goals: effectiveness, extensibility, cost, and scalability, using DFD models from the MicroSecEnD benchmark.
The approach eliminates all detected violations, produces repairs that are approximately 73\% less invasive than a human baseline, and scales acceptably across all studied dimensions of model complexity.
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Evaluierung von Embedding Modellen auf Modelldaten
| Vortragende(r)
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David Inca
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
|
Julian Roßkothen
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| Vortragssprache
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Deutsch
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| Vortragsmodus
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in Präsenz
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| Kurzfassung
|
Das zuverlässige Retrieval von Artefakten der modellgetriebenen Softwareentwicklung (MDSE) mittels semantischer Ähnlichkeit ist eine Kernvoraussetzung für leistungsfähige RAG-Systeme in diesem Bereich. Da gängige Embedding-Modelle primär für unstrukturierten Text optimiert sind, untersucht diese Arbeit deren Effektivität für strukturierte, referenzielle Modellartefakte. In einem kontrollierten Benchmark-Setting wird der Einfluss von Datenaufbereitung, Serialisierung und Embedding-Modellwahl systematisch evaluiert.
Die Ergebnisse verdeutlichen, dass die Wahl des Embedding-Modells den signifikantesten Einfluss auf die Retrievalqualität ausübt. Als besonders effektiv erweist sich die Dereferenzierung interner Verknüpfungen, während die Wahl des Serialisierungsansatzes nur eine marginale Rolle spielt. Die Untersuchung belegt die prinzipielle Eignung embedding-basierter Verfahren für MDSE-Daten und liefert konkrete Handlungsempfehlungen für die Konfiguration effizienter Retrieval-Architekturen.
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Textual Modeling for Cloud-Native Performance Simulation
| Vortragende(r)
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Fabio Freund
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| Vortragstyp
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Bachelorarbeit
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| Betreuer(in)
|
Maximilian Hummel
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| Vortragssprache
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Deutsch
|
| Vortragsmodus
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in Präsenz
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| Kurzfassung
|
Text-based modeling simplifies the creation of software architecture models, yet existing grammars are largely rooted in traditional PCM concepts. Modern cloud-native systems—built around containers, microservices, and Kubernetes-based workflows—do not align well with these abstractions. In addition, current modeling approaches lack an accessible, declarative syntax familiar to DevOps engineers who work with YAML-style configuration files. This thesis addresses this gap by extending an existing textual modeling language to better represent cloud-native patterns while introducing a concise, YAML-inspired syntax. The work includes analyzing and adapting the TPCM/Xtext grammar, designing user-friendly constructs aligned with real-world deployment descriptors, and implementing a transformation layer that maps the extended language to PCM models compatible with Palladio and Simulizer. The result will improve the usability and relevance of performance simulation in cloud-native environments.
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