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New directions in worst-case execution time analysis
University of York.ORCID iD: 0000-0003-2415-8219
University of York.
2008 (English)In: 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 2008, p. 3545-3552Conference paper, Published paper (Refereed)
Abstract [en]

Most software engineering methods require some form of model populated with appropriate information. Real-time systems are no exception. A significant issue is that the information needed is not always freely available and derived it using manual methods is costly in terms of time and money. Previous work showed how machine learning information derived during software testing can be used to derive loop bounds as part of the Worst-Case Execution Time analysis problem. In this paper we build on this work by investigating the issue of branch prediction.

Place, publisher, year, edition, pages
2008. p. 3545-3552
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23790DOI: 10.1109/CEC.2008.4631277ISI: 000263406502051Scopus ID: 2-s2.0-55749109671ISBN: 9781424418237 (print)OAI: oai:DiVA.org:mdh-23790DiVA, id: diva2:682464
Conference
2008 IEEE Congress on Evolutionary Computation, CEC 2008; Hong Kong; China; 1 June 2008 through 6 June 2008
Available from: 2013-12-27 Created: 2013-12-19 Last updated: 2014-05-19Bibliographically approved

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Bate, Iain

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