Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance Cases

Kimya Khakzad Shahandashti, Mithila Sivakumar, Mohammad Mahdi Mohajer, Alvine Boaye Belle, Song Wang, Timothy Lethbridge

Abstract: Assurance cases (ACs) are structured arguments that allow verifying the correct implementation of the created systems' non-functional requirements (e.g., safety, security). This allows for preventing system failure. The latter may result in catastrophic outcomes (e.g., loss of lives). ACs support the certification of systems in compliance with industrial standards, e.g., DO-178C and ISO 26262. Identifying defeaters ---arguments that challenge these ACs --- is crucial for enhancing ACs' robustness and confidence. To automatically support that task, we propose a novel approach that explores the potential of GPT-4 Turbo, an advanced Large Language Model (LLM) developed by OpenAI, in identifying defeaters within ACs formalized using the Eliminative Argumentation (EA) notation. Our preliminary evaluation assesses the model's ability to comprehend and generate arguments in this context and the results show that GPT-4 turbo is very proficient in EA notation and can generate different types of defeaters.