Prompting GPT-4 to support automatic safety case generation

In the ever-evolving field of software engineering, the advent of large language models and conversational interfaces, exemplified by ChatGPT, represents a significant revolution. While their potential is evident in various domains, this paper expands upon our previous research, where we experimented with GPT -4, on its ability to create safety cases. A safety case is a structured argument supported by a body of evidence to demonstrate that a given system is safe to operate in a given environment. In this paper, we first determine GPT -4's comprehension of the Goal Structuring Notation (GSN), a well-established notation for visually representing safety cases. Additionally, we conduct four distinct experiments using GPT -4 to evaluate its ability to generate safety cases within a specified system and application domain. To assess GPT -4's performance in this context, we compare the results it produces with the ground-truth safety cases developed for an X-ray system, a machine learning-enabled component for tire noise recognition in a vehicle, and a lane management system from the automotive domain. This comparison enables us to gain valuable insights into the model's generative capabilities. Our findings indicate that GPT -4 is able to generate moderately accurate and reasonable safety cases.