Assurance cases (ACs) are structured arguments that support the verification of the correct implementation of systems' non-functional requirements, such as safety and security, thereby preventing system failures which could lead to catastrophic outcomes, including loss of lives. ACs facilitate the certification of systems in accordance with industrial standards, for example, DO-178C and ISO 26262. Identifying defeaters arguments that refute these ACs is essential for improving the robustness and confidence in ACs. To automate this task, we introduce a novel method that leverages the capabilities of GPT-4 Turbo, an advanced Large Language Model (LLM) developed by OpenAI, to identify defeaters within ACs formalized using the Eliminative Argumentation (EA) notation. Our initial evaluation gauges the model's proficiency in understanding and generating arguments within this framework. The findings indicate that GPT-4 Turbo excels in EA notation and is capable of generating various types of defeaters.

翻译：保障案例（ACs）是支持系统非功能性需求（如安全性与可靠性）正确实现验证的结构化论证，从而防止可能导致灾难性后果（包括人员伤亡）的系统故障。ACs有助于系统依据行业标准（如DO-178C和ISO 26262）进行认证。识别反驳这些ACs的反驳论据对于提升ACs的鲁棒性和可信度至关重要。为自动化这一任务，我们提出了一种新颖方法，利用OpenAI开发的先进大规模语言模型GPT-4 Turbo的能力，在采用消除性论证（EA）符号形式化的ACs中识别反驳论据。初步评估衡量了模型在该框架内理解并生成论证的能力。结果表明，GPT-4 Turbo在EA符号方面表现出色，并能生成多种类型的反驳论据。