Boaye Belle, AlvineKhakzad Shahandashti, Kimya2024-07-182024-07-182024-04-252024-07-18https://hdl.handle.net/10315/42188Assurance cases are structured arguments that allow verifying the correct implementation of the created systems’ non-functional requirements (e.g., safety, security, reliability). This allows for preventing system failure. The latter may result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Assurance cases support the certification of systems in compliance with industrial standards (e.g., DO-178C, ISO26262). However, the presence of assurance weakeners - deficits and logical fallacies - signals gaps in evidence and reasoning. Addressing this, our research presents a comprehensive taxonomy for categorizing these assurance weakeners, alongside proposed management strategies. The taxonomy divides weakeners into four categories of uncertainty: aleatory, epistemic, ontological, and argumentation. It also categorizes management approaches into representation, identification, and mitigation. A critical aspect of strengthening assurance cases involves identifying argumentation uncertainty or defeaters. To automate this process, we explore the capabilities of GPT-4 Turbo, a sophisticated large language model by OpenAI. We focus on its application in detecting defeaters within assurance cases represented using Eliminative Argumentation(EA) notation. Our initial evaluation assesses GPT-4 Turbo’s proficiency in understanding and applying this notation, a key factor in effectively generating defeaters. The results indicate that GPT-4 Turbo is highly adept in EA notation, demonstrating its potential to generate a diverse range of defeaters, thereby enhancing the robustness and reliability of assurance cases. Moreover, we used GPT-4 Turbo to identify defeaters which demonstrated effective proficiency.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2024-07-18Assurance casesAssurance deficitDefeatersLLMs