Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.

中文翻译：

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量化学习型系统中的保证

嵌入机器学习 (ML) 组件的系统的可靠性保证——所谓的支持学习的系统 (LES)——是它们在安全关键应用程序中使用的关键步骤。在新兴的标准化和指导工作中，人们越来越认同为此目的使用保证案例的价值。本文提出了 LES 可靠保证的定量概念，作为其保证案例的核心组成部分，也扩展了我们先前应用于 ML 组件的工作。具体而言，我们以保证措施的形式描述 LES 保证：对 LES 拥有与功能能力和可靠性属性相关的系统级属性的置信度进行概率量化。我们通过应用于现实世界的自主航空系统来说明保障措施的效用，