# Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems

arXiv: https://arxiv.org/abs/2205.11060 · HTML: https://mlsanity.com/paper/2205.11060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [Online GANs for Automatic Performance Testing](https://mlsanity.com/paper/2104.11069/) (2021)

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

- [Requirement falsification for cyber-physical systems using generative models](https://mlsanity.com/paper/2310.20493/) (2023)
- [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 propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.