Design Space Evaluation for Confidentiality under Architectural Uncertainty

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Vortragende(r) Oliver Liu
Vortragstyp Bachelorarbeit
Betreuer(in) Sebastian Hahner
Termin Fr 15. Oktober 2021
Kurzfassung 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.