Changes and updates in the requirement artifacts, which can be frequent in
the automotive domain, are a challenge for SafetyOps. Large Language Models
(LLMs), with their impressive natural language understanding and generating
capabilities, can play a key role in automatically refining and decomposing
requirements after each update. In this study, we propose a prototype of a
pipeline of prompts and LLMs that receives an item definition and outputs
solutions in the form of safety requirements. This pipeline also performs a
review of the requirement dataset and identifies redundant or contradictory
requirements. We first identified the necessary characteristics for performing
HARA and then defined tests to assess an LLM's capability in meeting these
criteria. We used design science with multiple iterations and let experts from
different companies evaluate each cycle quantitatively and qualitatively.
Finally, the prototype was implemented at a case company and the responsible
team evaluated its efficiency.

基于大型语言模型的原型管道可以在每次更新后自动细化和分解需求，还能对需求数据集进行审查，找出冗余或矛盾的需求，该研究通过多个迭代的设计科学，让不同公司的专家从定量和定性两个方面评估每个周期，最终在一个案例公司中实施了原型，并由负责团队评估其效率。