Work

CoDefeater: Using LLMs To Find Defeaters in Assurance Cases

Year: 2024

Type: conference-paper

Abstract: Constructing assurance cases is a widely used and sometimes required process toward demonstrating that safety-critical systems will operate safely in their planned environment. To mitigate the risk of errors and missing edge cases, the concept of defeaters - challenges to claims in an assurance case - has been introduced. Defeaters can detect weaknesses in the arguments, prompting further investigation and timely mitigations. However, capturing defeaters relies on expert judgment, experience, and creativity and must be done iteratively due to evolving requirements and regulations. In this paper, we propose CoDefeater, an automated process to leverage large language models (LLMs) for finding defeaters. Initial results on two systems show that LLMs can efficiently find known and unforeseen feasible defeaters to support safety analysts in enhancing the completeness and confidence of assurance cases.

Source: IEEE/ACM International Conference on Automated Software Engineering (ASE)

DOI: 10.1145/3691620.3695296

Authors: Usman Gohar, Michael C. Hunter, Robyn R. Lutz, Myra B. Cohen

Institution: Iowa State University

Language: English

Topic: Safety Systems Engineering in Autonomy

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Aeronautics and Space Administration, Nuclear Safety and Security Commission

Award: G1660015768