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

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationThe 15th Search-Based Software Testing Workshop SBST 2022
PublisherIEEE
Pages1-5
ISBN (Print)978-1-4503-9318-8
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Search-Based Software Testing -
Duration: 9 May 2022 → …

Conference

ConferenceInternational Workshop on Search-Based Software Testing
Abbreviated titleSBST
Period09/05/22 → …

Fingerprint

Dive into the research topics of 'Wasserstein generative adversarial networks for online test generation for cyber physical systems'. Together they form a unique fingerprint.

Cite this