The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

Due to various trends in the automotive sector, such as autonomous driving and electrification, the number of Electric/Electronic (E/E) components has risen in both hardware and software. This has led to an increase in certification requirements, which cannot be fulfilled without simulation anymore [1]. Different approaches have emerged trying to master this issue. However, for supporting early design decisions in the E/E development, these are either domain-specific or too elaborate. In this paper, we demonstrate an approach to realize early design decisions through a cross-domain simulation of E/E architectures, regarding the environment, scenarios, vehicle physics, the scheduling of software components and the power supply net. We use static E/E architecture hardware models, consisting of Electronic Control Units (ECUs), sensors, actuators and the wiring harness, as the base for the structure of our simulation models. The individual E/E components are linked to parameterizable simulation model templates to facilitate scalable execution. Moreover, scenarios are used for model reduction and supply the simulation model with stimuli. The simulation model is synthesized in an automated manner. For the evaluation, we simulate the power consumption of an electric vehicle, dependent on different loads. It shows that considering hardware aspects in early design phases uncovers errors that would have been noticed much later, e.g. when using virtual Hardware In the Loop (vHIL) methods. We also investigate the scalability of our approach. As E/E architecture modeling tool, we use Vector PREEvision and for the simulation Mathworks Simulink.

Although fault tree analysis is well established in the industry and its application is proposed by the international standard for functional safety for road vehicles - ISO 26262 - it is often time intensive to perform. Tool chains are long, the line of action is not straightforward, annotations in data models are complex and traceability is not self-evident. To overcome these inconveniences, this paper presents an approach for graphically notated failure modeling, based on an integrated data model for electric and electronic architectures and rapidly performing of fault tree analysis during concept phase of system design. We demonstrate the utilization of existing concepts in the domain of electric and electronic architecture modeling to enrich existing architectures with concepts of fault tree analysis. Also, we demonstrate the model-based specification of failure models, their relation to modeling artifacts of electric and electronic architectures and the automated processing of failure expressions without the necessity to leave the development environment for electric and electronic architecture modeling. The presented methodology supports closer links between the development of architectures for safety-critical systems and failure analysis by facilitating traceability between failure and system modeling based on a common data structure and well-established modeling approaches.