When modelling software components for timing analysis, we typically encounter functional chains of tasks that lead to precedence relations. As these task chains represent a functionally-dependent sequence of operations, in real-time systems, there is usually a requirement for their end-to-end latency. When mapped to software components, functional chains often result in communicating threads. Since threads are scheduled rather than tasks, specific task chain properties arise that can be exploited for response-time analysis. As a core contribution, this paper presents an extension of the busy-window analysis suitable for such task chains in static-priority preemptive systems. We evaluated the extended busy-window analysis in a compositional performance analysis using synthetic test cases and a realistic automotive use case showing far tighter response-time bounds than current approaches.

Self-awareness has been used in many research fields in order to add autonomy to computing systems. In automotive systems, we face several system layers that must be enriched with self-awareness to build truly autonomous vehicles. This includes functional aspects like autonomous driving itself, its integration on the hardware/software platform, and among others dependability, real-time, and security aspects. However, self-awareness mechanisms of all layers must be considered in combination in order to build a coherent vehicle self-awareness that does not cause conflicting decisions or even catastrophic effects. In this paper, we summarize current approaches for establishing self-awareness on those layers and elaborate why self-awareness needs to be addressed as a cross-layer problem, which we illustrate by practical examples.

This paper addresses the challenges in managing the continuous change and evolution of CPSs and their operation environment. It presents two frameworks, controlling concurrent change (CCC) and information processing factory (IPF), for building self-aware CPSs that have the capabilities of self-modeling, self-configuration, and monitoring.

We present a framework based on constraint satisfaction that adds self-integration capabilities to componentbased embedded systems by identifying correct compositions of the desired components and their dependencies. This not only allows autonomous integration of additional functionality but can also be extended to ensure that the new configuration does not violate any extra-functional requirements, such as safety or security, imposed by the application domain.

The increasing complexity of automotive software systems and the desire for more frequent software and even feature updates require new approaches to the design, integration and testing of these systems. Ideally, those approaches enable an in-field updatability of automotive software systems that provides the same degree of safety guarantees as the traditionally labbased deployment. In this paper, we present a layered modelling approach that formalises the integration procedure of automotive software systems using graph-based models and formal analyses.