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：将机器学习集成到信息物理系统中带来了显著优势，包括增强效率、预测能力、实时响应性以及实现自主操作。这一融合加速了自动驾驶车辆、配送无人机、服务机器人和远程医疗程序等众多实际应用的开发与部署。然而，面向人工智能赋能的信息物理系统的软件开发生命周期与传统方法存在显著差异，其中数据和学习成为两个关键组成部分。现有的验证与确认技术往往不足以应对这些新范式。在本研究中，我们指出了确保学习型信息物理系统形式安全性的主要挑战。我们首先将测试视为最实用的验证与确认方法，并总结了当前最先进的方法论。鉴于现有测试方法在提供形式安全保证方面的局限性，我们提出了一条从基础概率测试转向能够提供形式保证的更严格方法的路线图。