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

Autor:	Peltomäki, Jarkko, Spencer, Frankie, Porres, Ivan
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Software Engineering
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.11060 Zobrazit plný text záznamu View this record from Arxiv