Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems
This addresses the need for efficient testing methods in cyber-physical systems, but it is incremental as it builds on existing algorithms with a competitive rather than superior result.
The paper tackles the problem of online test generation for cyber-physical systems by proposing WOGAN, a black-box test generator based on Wasserstein Generative Adversarial Networks, and demonstrates its competitive performance in generating roads that cause a lane assistance system to fail.
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.