Towards simplification of failure scenarios for machine learning-enabled autonomous systems

Shin, D. orcid.org/0000-0002-0840-6449 and Pennada, S. (2024) Towards simplification of failure scenarios for machine learning-enabled autonomous systems. In: 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), 01-05 Jul 2024, Cambridge, United Kingdom. Institute of Electrical and Electronics Engineers (IEEE) , pp. 1089-1090. ISBN 9798350365665

Abstract

Scenario-based testing is an essential way of im-proving the safety and reliability of machine learning-enabled autonomous systems (MLAS), such as autonomous driving systems (ADS). As the complexity of failure scenarios in-creases with the development of more realistic MLAS testing approaches, it becomes essential to simplify failure scenarios to understand and identify the root causes of failures. In this vision paper, we present our vision to leverage search-based software engineering (SBSE) and surrogate-assisted optimisation (SAO) to address the challenges of simplifying failure scenarios in MLAS.

Metadata

Item Type:	Proceedings Paper
Authors/Creators:	Shin, D. https://orcid.org/0000-0002-0840-6449 Pennada, S.
Copyright, Publisher and Additional Information:	© 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C) 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:	Autonomous Systems; Software Testing; Scenario Simplification
Dates:	Published: 29 October 2024 Published (online): 29 October 2024
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:	Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/Y014219/1
Depositing User:	Symplectic Sheffield
Date Deposited:	06 Nov 2024 17:01
Last Modified:	06 Nov 2024 17:01
Status:	Published
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
Identification Number:	10.1109/qrs-c63300.2024.00143
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:219323

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Towards simplification of failure scenarios for machine learning-enabled autonomous systems

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