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Falsification of Cyber-physical Systems Using Bayesian Optimization

Ramezani, Zahra ; Šehić, Kenan LU orcid ; Nardi, Luigi LU and Åkesson, Knut (2025) In ACM Transactions on Embedded Computing Systems 24(3).
Abstract

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... (More)

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.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
bayesian optimization, Cyber-physical systems, falsification, testing
in
ACM Transactions on Embedded Computing Systems
volume
24
issue
3
article number
41
publisher
Association for Computing Machinery (ACM)
external identifiers
  • scopus:105005524453
ISSN
1539-9087
DOI
10.1145/3711922
language
English
LU publication?
yes
id
b9d320c6-d2b8-4ae6-8658-450eb4b81522
date added to LUP
2025-08-12 09:40:28
date last changed
2025-08-12 09:41:29
@article{b9d320c6-d2b8-4ae6-8658-450eb4b81522,
  abstract     = {{<p>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.</p>}},
  author       = {{Ramezani, Zahra and Šehić, Kenan and Nardi, Luigi and Åkesson, Knut}},
  issn         = {{1539-9087}},
  keywords     = {{bayesian optimization; Cyber-physical systems; falsification; testing}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  series       = {{ACM Transactions on Embedded Computing Systems}},
  title        = {{Falsification of Cyber-physical Systems Using Bayesian Optimization}},
  url          = {{http://dx.doi.org/10.1145/3711922}},
  doi          = {{10.1145/3711922}},
  volume       = {{24}},
  year         = {{2025}},
}