一句话：提出 CoDefeater，用 LLM 自动发现安全保证案例中的 defeater，提升案例完整性与可信度。
安全关键系统的保证案例（assurance case）构建常需论证系统在预期环境中安全运行，而 defeater 作为对案例中主张的挑战，能揭示论证弱点、促进及时调查与缓解。然而，捕捉 defeater 高度依赖专家判断、经验与创造力，且因需求与法规演变必须迭代进行。本文提出 CoDefeater，一种利用大语言模型（LLM）自动寻找 defeater 的流程。作者在两个系统上进行了初步评估，结果表明 LLM 能够高效发现已知的以及未预见的可行 defeater，从而帮助安全分析师提升保证案例的完整性与置信度。
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.CCS CONCEPTS• Software and its engineering→Software safety; Requirements analysis; • Computing methodologies→Artificial intelligence.