  In the ever-evolving landscape of software engineering, the emergence of
large language models (LLMs) and conversational interfaces, exemplified by
ChatGPT, is nothing short of revolutionary. While their potential is undeniable
across various domains, this paper sets out on a captivating expedition to
investigate their uncharted territory, the exploration of generating safety
cases. In this paper, our primary objective is to delve into the existing
knowledge base of GPT-4, focusing specifically on its understanding of the Goal
Structuring Notation (GSN), a well-established notation allowing to visually
represent safety cases. Subsequently, we perform four distinct experiments with
GPT-4. These experiments are designed to assess its capacity for generating
safety cases within a defined system and application domain. To measure the
performance of GPT-4 in this context, we compare the results it generates with
ground-truth safety cases created for an X-ray system system and a
Machine-Learning (ML)-enabled component for tire noise recognition (TNR) in a
vehicle. This allowed us to gain valuable insights into the model's generative
capabilities. Our findings indicate that GPT-4 demonstrates the capacity to
produce safety arguments that are moderately accurate and reasonable.
Furthermore, it exhibits the capability to generate safety cases that closely
align with the semantic content of the reference safety cases used as
ground-truths in our experiments.
