Using GPT-4 Turbo to Automatically Identify Defeaters in Assurance Cases

Assurance cases (ACs) are convincing arguments, supported by a body of evidence and aiming at demonstrating that a system will function as intended. Producers of systems can rely on assurance cases to demonstrate to regulatory authorities how they have complied with existing industrial standards (e.g., ISO 26262, DO-178C). Defeaters are arguments that challenge the effectiveness of assurance cases. Their presence in assurance cases could compromise the reliability of these assurance cases and make them inadequate for verifying a system's capabilities (e.g., safety, and security). This may lead to system failure, which could have severe outcomes, including loss of life. Therefore, identifying and mitigating defeaters is key to improving assurance cases robustness and reliability. In this paper, we focus on the identification of defeaters. Thus, we rely on GPT-4 Turbo, a Large Language Model developed by OpenAI, to automate the generation (identification) of defeaters in assurance cases. Our approach uses the Eliminative Argumentation (EA) notation to represent assurance cases. Besides, we leverage the Chain of Thought prompting technique to improve GPT-4 Turbo's reasoning capabilities. We conducted experiments on various reference assurance case fragments from the nuclear and aviation domains to evaluate the ability of GPT-4 Turbo to automatically generate defeaters. Although the quality of our experiments results is relatively moderate, the analysis of these results still provides valuable insights on the effectiveness of GPT-4 Turbo in generating defeaters.