Abstract
Verification, validation, and testing of complex cyber-physical systems, such as autonomous
vehicles, have traditionally relied on document-intensive processes and physical prototypes. However, accelerated development timelines increasingly challenge the feasibility of these approaches. Model-based
systems engineering (MBSE) offers a model-centric alternative that enables new forms of system analysis.
This article presents a graph-based methodology for evaluating system modeling language system models
using functional flow block diagrams and interface definitions. By transforming system functions into
a functional dependence graph and applying community detection techniques, we derive three structural
metrics: system complexity, system modularity, and system test effort. The methodology is applied to three
case studies, an academic autonomous mobile robot and two industrial automotive propulsion systems, to
explore its potential utility. Across these examples, the graph-based approach yielded improvements in all
three metrics compared to traditional subject-matter-expert-based partitioning. The findings suggest that
graph-based evaluation may support early-stage architectural reasoning and model refactoring in MBSE
workflows, with further validation needed to confirm its broader applicability and practical impact.
vehicles, have traditionally relied on document-intensive processes and physical prototypes. However, accelerated development timelines increasingly challenge the feasibility of these approaches. Model-based
systems engineering (MBSE) offers a model-centric alternative that enables new forms of system analysis.
This article presents a graph-based methodology for evaluating system modeling language system models
using functional flow block diagrams and interface definitions. By transforming system functions into
a functional dependence graph and applying community detection techniques, we derive three structural
metrics: system complexity, system modularity, and system test effort. The methodology is applied to three
case studies, an academic autonomous mobile robot and two industrial automotive propulsion systems, to
explore its potential utility. Across these examples, the graph-based approach yielded improvements in all
three metrics compared to traditional subject-matter-expert-based partitioning. The findings suggest that
graph-based evaluation may support early-stage architectural reasoning and model refactoring in MBSE
workflows, with further validation needed to confirm its broader applicability and practical impact.
| Original language | English |
|---|---|
| Number of pages | 13 |
| Journal | IEEE Open Journal of Systems Engineering |
| DOIs | |
| Publication status | Published - 4 Nov 2025 |
Acknowledgements
The authors want to express sincere gratitude to J. Padget,C. Brace, and P. Ebner for their collaboration and insightful
contributions throughout the development of this work. Their
expertise and guidance have been instrumental in shaping the
direction and quality of this research.