Falsification of Cyber-Physical Systems using Bayesian Optimization
Journal article, 2025

Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system’s specifications are met. Simulation-based falsification is a practical testing technique for increasing confidence in a CPS’s correctness, as it only requires that the system be simulated. Reducing the number of computationally intensive simulations needed for falsification is a key concern. In this study, we investigate Bayesian optimization (BO), a sample-efficient approach that learns a surrogate model to capture the relationship between input signal parameterization and specification evaluation. We propose two enhancements to the basic BO for improving falsification: (1) leveraging local surrogate models, and (2) utilizing the user’s prior knowledge. Additionally, we address the formulation of acquisition functions for falsification by proposing and evaluating various alternatives. Our benchmark evaluation demonstrates significant improvements when using local surrogate models in BO for falsifying challenging benchmark examples. Incorporating prior knowledge is found to be especially beneficial when the simulation budget is constrained. For some benchmark problems, the choice of acquisition function noticeably impacts the number of simulations required for successful falsification.

Testning

Cyber-Physcial Systems

Author

Zahra Ramezani

Chalmers, Electrical Engineering, Systems and control

Knut Åkesson

Chalmers, Electrical Engineering, Systems and control

Transactions on Embedded Computing Systems

1539-9087 (ISSN) 15583465 (eISSN)

Systematic testing of cyber-physical systems (SyTeC)

Swedish Research Council (VR) (2016-06204), 2017-01-01 -- 2022-12-31.

Subject Categories (SSIF 2025)

Robotics and automation

Embedded Systems

Control Engineering

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1145/3711922

More information

Latest update

3/31/2025