一句话:面向 Simulink CPS 数据流模型,提出从故障注入、定位到变异测试与覆盖准则的整体故障诊断方法。
该博士研究针对安全攸关信息物理系统(CPS)中未检测故障代价高甚至危及生命的问题,围绕 Simulink 数据流模型开展基于故障的测试与诊断,并以严格形式规范进行验证。工作提出五项贡献:系统化故障注入的自动化工具、受生物启发的全局优化算法、鲁棒的故障定位方法、面向形式性质评估测试套件的变异测试新方法,以及为 CPS 数据流模型定制的新覆盖率准则。作者在多个领域的公开基准上评估这些工具和技术,结果表明其相较现有方法能更充分地覆盖各类测试场景,提升故障检测、解释与修复能力,为构建更鲁棒的 CPS 奠定基础。
Cyber-Physical Systems (CPSs) face growing complexity, especially in safety-critical areas. Ensuring their correctness is vital to maintain full operational capacity, as undetected failures can be both costly and life-threatening. Therefore, advanced fault diagnosis procedures are essential for thorough CPS testing, enabling accurate fault detection, explanation, and rectification. This doctoral research contributes to the field by developing novel tools and techniques to enhance fault-based testing and diagnosis of CPSs. Our research focuses on testing of CPS dataflow models created in Simulink, validated against strict formal specifications. Our contributions include (i) an automated tool for systematic fault injection, (ii) a bio-inspired global optimization algorithm, (iii) a robust fault localization method, (iv) a novel approach to mutation testing for evaluating test suites against formal properties, and (v) a new coverage criterion tailored for CPS dataflow models. This comprehensive approach offers significant improvements over existing methods, ensuring thorough testing across various scenarios. We validate the effectiveness of our solutions using publicly available benchmarks from various domains. Our findings open new perspectives on CPS testing, laying the foundation for more robust CPSs