The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations. This convergence has accelerated the development and deployment of a range of real-world applications, such as autonomous vehicles, delivery drones, service robots, and telemedicine procedures. However, the software development life cycle (SDLC) for AI-infused CPS diverges significantly from traditional approaches, featuring data and learning as two critical components. Existing verification and validation techniques are often inadequate for these new paradigms. In this study, we pinpoint the main challenges in ensuring formal safety for learningenabled CPS.We begin by examining testing as the most pragmatic method for verification and validation, summarizing the current state-of-the-art methodologies. Recognizing the limitations in current testing approaches to provide formal safety guarantees, we propose a roadmap to transition from foundational probabilistic testing to a more rigorous approach capable of delivering formal assurance.

翻译：机器学习（ML）与网络物理系统（CPS）的融合带来了显著收益，包括增强效率、预测能力、实时响应性以及支持自主运行。这一融合加速了智能车辆、递送无人机、服务机器人和远程医疗程序等实际应用的开发与部署。然而，注入人工智能的CPS软件开发生命周期（SDLC）与传统方法存在显著差异，其核心组件包括数据与学习。现有的验证与确认技术往往难以适用于这些新范式。本研究首先识别了为具备学习能力的CPS提供形式化安全保障的主要挑战。我们以测试作为最实用的验证与确认方法为切入点，总结了当前前沿方法论。针对现有测试方法在提供形式化安全保证方面的局限性，我们提出了一条从基础概率测试过渡至更严格、可提供形式化保证方法的路线图。