Work

Wasserstein generative adversarial networks for online test generation for cyber physical systems

Year: 2022

Type: conference-paper

Abstract: 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.

DOI: 10.1145/3526072.3527522

Authors: Jarkko Peltomäki, Frankie Spencer, Ivan Porres

Institution: Åbo Akademi University

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: European Commission, Electronic Components and Systems for European Leadership

Awards: G434337789, G6618363488, AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems

OpenAlex ID: W4319150769

FWCI: 2.072

Cites: 6

Cited by: 11

Related to: 10