mlsanityData to 14 Oct 2026

Requirement falsification for cyber-physical systems using generative models

arXiv 2310.20493 ↗First posted 31 Oct 2023cs.LG cs.SEcited by 3

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Abstract (arXiv)

We present the OGAN algorithm for automatic requirement falsification of cyber-physical systems. System inputs and outputs are represented as piecewise constant signals over time while requirements are expressed in signal temporal logic. OGAN can find inputs that are counterexamples for the correctness of a system revealing design, software, or hardware defects before the system is taken into operation. The OGAN algorithm works by training a generative machine learning model to produce such counterexamples. It executes tests offline and does not require any previous model of the system under test. We evaluate OGAN using the ARCH-COMP benchmark problems, and the experimental results show that generative models are a viable method for requirement falsification. OGAN can be applied to new systems with little effort, has few requirements for the system under test, and exhibits state-of-the-art CPS falsification efficiency and effectiveness.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 31 Oct 2023.

Builds on

18 of 51 references.

References · 18

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2016The CMA Evolution Strategy: A Tutorialcited by 377
  5. 2023Diffusion Models for Black-Box Optimizationcited by 76
  6. 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
  7. 2020On Robustness Metrics for Learning STL Taskscited by 27
  8. 2020A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Controlcited by 21
  9. 2018Falsification of Cyber-Physical Systems Using Deep Reinforcement Learningcited by 17
  10. 2019Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identificationcited by 8
  11. 2020Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checkingcited by 7
  12. 2021Online GANs for Automatic Performance Testingcited by 6
6 more
  1. 2021Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guaranteescited by 6
  2. 2021PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systemscited by 5
  3. 2022Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functionscited by 5
  4. 2022Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systemscited by 3
  5. 2022Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Banditscited by 2
  6. 2022WOGAN at the SBST 2022 CPS Tool Competitioncited by 2

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systemscited by 6
  2. 2024Learning test generators for cyber-physical systemscited by 0
  3. 2025Data-Driven Falsification of Cyber-Physical Systemscited by 0