用生成式AI+模型驱动，自动发现安全需求中的矛盾与遗漏。
Foundational Analysis of Safety Engineering Requirements (SAFER)
- 通过生成式AI分析需求模型，自动映射需求与系统功能
- 在无人机系统中提升不一致需求检测率，效率与可靠性显著增强
- 适合安全工程团队用于早期识别风险，避免后期返工
我们提出一种基础性安全需求分析框架SAFER，一种基于模型的生成式AI方法，用于提升复杂安全关键系统的安全需求生成与分析质量。安全需求常由多个利益相关方提出，目标不一致导致需求缺失、重复和矛盾，危及系统安全与合规性。现有方法多为非正式手段，难以应对此类挑战。SAFER通过消费需求规格模型，实现：（1）将需求映射至系统功能；（2）识别需求不充分的功能；（3）检测重复需求；（4）发现需求集内的矛盾。SAFER提供结构化分析、报告与决策支持。我们在自主无人机系统上验证了该方法，显著提升了需求不一致性的检测能力，增强了安全工程流程的效率与可靠性。研究证明，生成式AI必须结合形式化模型并系统化查询，才能在早期阶段生成有意义的安全需求与稳健安全架构。
原文摘要 · Abstract (English)
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.
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