X-By-Construction design framework for engineering autonomous & distributed real-time embedded software systems
The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, deployment of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems relying on Machine Learning (ML) and AI applications in combination with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are autonomous driving cars that are prone to various safety/security vulnerabilities. The XANDAR project is built to exactly match the goals defined within the ICT-50 Software Technologies.
XANDAR will deliver a mature SW toolchain (from requirements capture down to the actual code integration on target including V&V) fulfilling the needs of the industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage novel automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel real-time, safety-, and security-by-Construction (X-by-Construction) paradigm. For the first time, XbC-guided code generation for non-deterministic ML/AI applications will be combined with novel runtime monitors to ensure fail-operation in the presence of runtime faults and security exploitations. The project provides a consortium covering the full spectrum of ES and software engineering. XANDAR will be validated by an automotive OEM (BMW) and the German Aerospace Center (DLR). Leading European SMEs and enterprises such as Vector, AVN, and fentISS as well as successful academic partners will contribute their diverse knowhow in Model-Driven Engineering, Software Systems and V&V, multicore architectures, code generation, and security enforcements from higher-level behavioral models to actual runnables.