Falsification of Cyber-Physical Systems using Bayesian Optimization
This work addresses the efficiency of testing safety-critical cyber-physical systems, which is crucial for engineers and developers, though it appears incremental by building on existing Bayesian optimization methods.
The study tackled the problem of reducing the number of computationally intensive simulations needed for falsification in cyber-physical systems by investigating Bayesian optimization with enhancements like local surrogate models and prior knowledge, resulting in significant improvements for challenging benchmarks, such as reduced simulation counts in some cases.
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.