# Requirement falsification for cyber-physical systems using generative models

arXiv: https://arxiv.org/abs/2310.20493 · HTML: https://mlsanity.com/paper/2310.20493/

## 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 (most cited first, 18 of 18)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Diffusion Models for Black-Box Optimization](https://mlsanity.com/paper/2306.07180/) (2023)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [On Robustness Metrics for Learning STL Tasks](https://mlsanity.com/paper/2003.06041/) (2020)
- [A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Control](https://mlsanity.com/paper/2006.05239/) (2020)
- [Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/1805.00200/) (2018)
- [Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identification](https://mlsanity.com/paper/1910.02837/) (2019)
- [Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking](https://mlsanity.com/paper/2005.02126/) (2020)
- [Online GANs for Automatic Performance Testing](https://mlsanity.com/paper/2104.11069/) (2021)
- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems](https://mlsanity.com/paper/2106.02200/) (2021)
- [Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions](https://mlsanity.com/paper/2207.11016/) (2022)
- [Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems](https://mlsanity.com/paper/2205.11060/) (2022)
- [Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits](https://mlsanity.com/paper/2205.11057/) (2022)
- [WOGAN at the SBST 2022 CPS Tool Competition](https://mlsanity.com/paper/2205.11064/) (2022)

## Built on by (most cited first, 3 of 3)

- [Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems](https://mlsanity.com/paper/2406.14761/) (2024)
- [Learning test generators for cyber-physical systems](https://mlsanity.com/paper/2410.03202/) (2024)
- [Data-Driven Falsification of Cyber-Physical Systems](https://mlsanity.com/paper/2505.03863/) (2025)

## 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.