Synthesis and Exploration of Multi-Level, Multi-Perspective Architectures of Automotive Embedded System
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In industry, evaluating candidate architectures of automotive embedded systems is routinely done during the design process. Today's engineers, however, are limited in the number of candidates that they are able to evaluate in order to find the optimal architectures. This limitation results from the difficulty in defining the candidates as it is a mostly manual process. In this work, we propose a way to synthesize multi-level, multi-perspective candidate architectures and to explore them across the different layers and perspectives. Using a reference model similar to the EAST-ADL domain model but with a focus on early design, we explore the candidate architectures for two case studies: an automotive power window system and the central door locking system. Further, we provide a comprehensive set of questions, based on the different layers and perspectives, that engineers can ask to synthesize only the candidates relevant to their task at hand. Finally, using the modeling language Clafer, which is supported by automated backend reasoners, we show that it is possible to synthesize and explore optimal candidate architectures for two highly configurable automotive subsystems.
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Jordan Ross (2016). Synthesis and Exploration of Multi-Level, Multi-Perspective Architectures of Automotive Embedded System. UWSpace. http://hdl.handle.net/10012/10632