Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 23 May 2022.
Builds on
4 of 10 references.
References · 4
- 2022Generative Adversarial Networkscited by 5,603
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
- 2021Online GANs for Automatic Performance Testingcited by 6
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.