Theses

The automotive industry is undergoing a paradigm shift from hardware-centric engineering practices to software-defined, Cyber-Physical Systems (CPSs). This evolution is driven by consumer demand for increasingly automated and intuitive vehicle features, such as Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD), alongside the need for faster time-to-market and continuous functional updates. These trends have introduced unprecedented levels of system complexity, challenging traditional development and validation methodologies. In response to this paradigm shift, this thesis presents a model-based framework for managing complexity and enabling automated testing in the development of automotive CPSs.

The research uses Model-Based Systems Engineering (MBSE), which replaces traditional document-centric approaches with structured, queryable system models. A central contribution of this research is the development of a set of metrics computed by transforming static SysML models into Functional Dependency Graphs (FDGs), enabling graph-theoretic analysis of the system’s architecture. These include System Complexity, System Modularity, and System Test Effort.

Building on this evaluation, the research introduces a model-driven approach to test planning and execution. Falsification, where the system is deliberately challenged with scenarios designed to expose weaknesses or violations of requirements, is applied to facilitate the automatic generation of high-impact test cases, improving coverage and reducing the likelihood of undetected faults. Test prioritisation is introduced for large systems and guided by two indicators: the Function Maturity Rating and the Risk Rating, to manage large and complex systems. These metrics ensure that testing efforts are focused where they are most needed, minimising bias and improving Verification and Validation (V&V) efficiency.

Together, these contributions form a methodology for enhancing MBSE in the development of complex automotive CPSs. By bridging the gap between high-level system modelling and low-level testing, the framework supports early problem detection, continuous validation, and informed decision-making