Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance CasesNew Idea Paper
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.
Sun 14 AprDisplayed time zone: Lisbon change
11:00 - 12:30 | Foundation Models for Software Quality AssuranceResearch Track at Luis de Freitas Branco Chair(s): Matteo Ciniselli Università della Svizzera Italiana | ||
11:00 14mFull-paper | Deep Multiple Assertions GenerationFull Paper Research Track | ||
11:14 14mFull-paper | MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented GenerationFull Paper Research Track Guanyu Wang Beijing University of Posts and Telecommunications, Yuekang Li The University of New South Wales, Yi Liu Nanyang Technological University, Gelei Deng Nanyang Technological University, Li Tianlin Nanyang Technological University, Guosheng Xu Beijing University of Posts and Telecommunications, Yang Liu Nanyang Technological University, Haoyu Wang Huazhong University of Science and Technology, Kailong Wang Huazhong University of Science and Technology | ||
11:28 14mFull-paper | Planning to Guide LLM for Code Coverage PredictionFull Paper Research Track Hridya Dhulipala University of Texas at Dallas, Aashish Yadavally University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
11:42 7mShort-paper | The Emergence of Large Language Models in Static Analysis: A First Look through Micro-BenchmarksNew Idea Paper Research Track Ashwin Prasad Shivarpatna Venkatesh University of Paderborn, Samkutty Sabu University of Paderborn, Amir Mir Delft University of Technology, Sofia Reis Instituto Superior Técnico, U. Lisboa & INESC-ID, Eric Bodden | ||
11:49 14mFull-paper | Reality Bites: Assessing the Realism of Driving Scenarios with Large Language ModelsFull Paper Research Track Jiahui Wu Simula Research Laboratory and University of Oslo, Chengjie Lu Simula Research Laboratory and University of Oslo, Aitor Arrieta Mondragon University, Tao Yue Beihang University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University | ||
12:03 7mShort-paper | Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance CasesNew Idea Paper Research Track Kimya Khakzad Shahandashti York University, Mithila Sivakumar York University, Mohammad Mahdi Mohajer York University, Alvine Boaye Belle York University, Song Wang York University, Timothy Lethbridge University of Ottawa | ||
12:10 20mOther | Discussion Research Track | ||