Foundational Analysis of Safety Engineering Requirements (SAFER)
This addresses safety engineering challenges for stakeholders in safety-critical systems, though it appears incremental as it enhances existing Model-Based Systems Engineering approaches.
The paper tackles the problem of uncoordinated safety requirements in complex safety-critical systems by introducing the SAFER framework, which uses Generative AI and model-driven methods to map requirements, identify gaps, detect duplicates, and find contradictions, demonstrating improved detection of inconsistencies in an autonomous drone system.
We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.