Cyber-physical systems, used in domains such as avionics or medical devices, perform critical functions where a fault might have catastrophic consequences (mission failure, severe injuries, etc.). Their development is guided by rigorous practice standards to avoid any error. However, as software importance continues to grow, integration and validation activities are becoming overwhelming. Late discovery of errors introduced in requirements and architecture design have resulted in costly rework, making up as much as 70% of the total software system cost. To overcome these issues, architecture-centric model-based approaches abstract system concerns into models that are analyzed to detect errors, issues or defects that are usually detected lately in the development process. This predictive analysis approach is automated by tools, avoiding any tedious manual efforts. In this paper, we present our model-based approach for capture and analyze system safety and reliability. We have added an error behavior annotation to SAE AADL, a standard for modeling embedded software system architectures. We also implemented analysis methods to automate safety analysis and production of safety assessment reports, as requested by safety recommended practices standards (such as ARP4761).
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