SMT-based Probabilistic Analysis of Timing Constraints in - PowerPoint PPT Presentation

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SMT-based Probabilistic Analysis of Timing Constraints in Cyber-Physical Systems Li Huang 1 and Eun-Young Kang 12 , 1 School of Data & Computer Science, Sun Yat-sen University, China huangl223@mail2.sysu.edu.cn 2 PReCISE Research Centre,

SMT-based Probabilistic Analysis of Timing Constraints in Cyber-Physical Systems Li Huang 1 and Eun-Young Kang 12 , 1 School of Data & Computer Science, Sun Yat-sen University, China huangl223@mail2.sysu.edu.cn 2 PReCISE Research Centre, University of Namur, Belgium eykang@fundp.ac.be
Background & Motivation • Cyber Physical Systems (CPS), e.g., automotive systems, are real-time embedded systems. • CPS contain continuous dynamic behaviors and stochastic behaviors. • Timing constraints should be satisfied. • Most of CPS are weakly-hard real-time systems, i.e., timing constraints violations can be tolerated if their frequencies are bounded. 2
Research Question How to formally specify and verify timing constraints of weakly-hard CPS with stochastic behaviors? 3
Approach [1] Eun-Young Kang and Li Huang. "Probabilistic Analysis of Timing Constraints in Autonomous Automotive Systems using Simulink Design Verifier." In International Symposium on Dependable Software Engineering Theories, Tools and Applications (SETTA) , pp. 170-186, Springer, 2018. [2] Eun-Young Kang, Dongrui Mu, and Li Huang. "Probabilistic Verification of Timing Constraints in Automotive Systems using UPPAAL-SMC." In International Conference on Integrated Formal Methods (IFM), pp. 236-254, Springer, 2018. [3] Eun-Young Kang, Li Huang, and Dongrui Mu. "Formal Verification of Energy and Timed Requirements for a Cooperative Automotive System." In Symposium On Applied Computing in Software Engineering (SAC) , pp. 1492-1499, ACM, 2018. [4] Eun-Young Kang, Dongrui Mu, Li Huang, and Qianqing Lan. "Verification and Validation of a Cyber-Physical System in the Automotive Domain." In International Conference on Software Quality, Reliability and Security (QRS), pp. 326-333, IEEE, 2017. 4
Approach 5
Experiments: Verification of Timing Constraints using Z3 Solver Case studies: Cooperative Automotive System (CAS) and Autonomous Vehicle (AV) Probability Threshold: 95% Simulation Bound: 6000 Steps Fig. 1. Average analysis time for verifying ETC in Z3. The simulation bound (number of steps) ranges from 1000 to 6000 with increment as 1000. 6
On-going & Future work • Translation of Simulink/Stateflow model of physical plant: continuous dynamic behaviors, stochastic behaviors. • Integration of Z3PY encodings of continuous physical plant and discrete controller. • Development of a fully automatic verification tool chain. 7
Thank You 8

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