Given the safety-critical functions of autonomous cyber-physical systems (CPS) across diverse domains, testing these systems is essential. While conventional software and hardware testing methodologies offer partial insights, they frequently do not provide adequate coverage in a CPS. In this study, we introduce a testing framework designed to systematically formulate test cases, effectively exploring the state space of CPS. This framework introduces a coverage-centric sampling technique, coupled with a cluster-based methodology for training a surrogate model. The framework then uses model predictive control within the surrogate model to generates test cases tailored to CPS specifications. To evaluate the efficacy of the framework, we applied it on several benchmarks, spanning from a kinematic car to systems like an unmanned aircraft collision avoidance system (ACAS XU) and automatic transmission system. Comparative analyses were conducted against alternative test generation strategies, including randomized testing, as well as falsification using S-TaLiRo.

翻译：鉴于自主信息物理系统（CPS）在多个领域中的安全关键功能，对其进行测试至关重要。尽管传统的软件和硬件测试方法能提供部分见解，但它们往往无法在CPS中实现充分的覆盖。本研究提出了一种测试框架，旨在系统性地制定测试用例，有效探索CPS的状态空间。该框架引入了一种以覆盖为中心的采样技术，并结合基于聚类的代理模型训练方法。随后，框架在代理模型内利用模型预测控制生成符合CPS规范的测试用例。为评估该框架的有效性，我们将其应用于多个基准测试，涵盖从运动学小车到无人飞行器防撞系统（ACAS XU）及自动变速系统等系统。我们与替代测试生成策略（包括随机测试以及使用S-TaLiRo的虚假性测试）进行了对比分析。