Identifying the hazard boundary of ML-enabled autonomous systems using cooperative co-evolutionary search

Sharifi, S., Shin, D. orcid.org/0000-0002-0840-6449, Briand, L.C. et al. (1 more author) (2023) Identifying the hazard boundary of ML-enabled autonomous systems using cooperative co-evolutionary search. IEEE Transactions on Software Engineering, 49 (12). pp. 5120-5138. ISSN 0098-5589

Abstract

Metadata

Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Software Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: ML-enabled Autonomous System; Hazard Boundary; System Safety; Monitoring; Cooperative Co-Evolutionary Search
Dates:
  • Published: December 2023
  • Published (online): 7 November 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 10 Nov 2023 10:02
Last Modified: 09 Oct 2024 16:11
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/tse.2023.3327575
Related URLs:
Open Archives Initiative ID (OAI ID):

Export

Statistics