A Lean Approach to Building Valid Model-Based Safety Arguments

Torin Viger, Logan Murphy, Alessio Di Sandro, Ramy I. Shahin, M. Chechik
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引用次数: 3

Abstract

In recent decades, cyber-physical systems developed using Model-Driven Engineering (MDE) techniques have become ubiquitous in safety-critical domains. Safety assurance cases (ACs) are structured arguments designed to comprehensively show that such systems are safe; however, the reasoning steps, or strategies, used in AC arguments are often informal and difficult to rigorously evaluate. Consequently, AC arguments are prone to fallacies, and unsafe systems have been deployed as a result of fallacious ACs. To mitigate this problem, prior work [32] created a set of provably valid AC strategy templates to guide developers in building rigorous ACs. Yet instantiations of these templates remain error-prone and still need to be reviewed manually. In this paper, we report on using the interactive theorem prover Lean to bridge the gap between safety arguments and rigorous model-based reasoning. We generate formal, modelbased machine-checked AC arguments, taking advantage of the traceability between model and safety artifacts, and mitigating errors that could arise from manual argument assessment. The approach is implemented in an extended version of the MMINT-A model management tool [10]. Implementation includes a conversion of informal claims into formal Lean properties, decomposition into formal sub-properties and generation of correctness proofs. We demonstrate the applicability of the approach on two safety case studies from the literature.
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构建有效的基于模型的安全论证的精益方法
近几十年来,使用模型驱动工程(MDE)技术开发的网络物理系统在安全关键领域变得无处不在。安全保证案例(ACs)是结构化的论证,旨在全面表明此类系统是安全的;然而,在AC论证中使用的推理步骤或策略通常是非正式的,难以严格评估。因此,AC参数容易产生谬误,并且由于错误的AC而部署了不安全的系统。为了缓解这个问题,先前的工作[32]创建了一组可证明有效的AC策略模板,以指导开发人员构建严格的AC。然而,这些模板的实例化仍然容易出错,仍然需要手工检查。在本文中,我们报告了使用交互定理证明器Lean来弥合安全论证和严格的基于模型的推理之间的差距。我们生成正式的、基于模型的机器检查的AC参数,利用模型和安全工件之间的可追溯性,并减少可能由手工参数评估产生的错误。该方法在MMINT-A模型管理工具[10]的扩展版本中实现。实现包括将非正式的声明转换为正式的精益属性,将其分解为正式的子属性,并生成正确性证明。我们从文献中论证了该方法在两个安全案例研究中的适用性。
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