New Directions in Worst-Case Execution Time Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8
Place of PublicationNEW YORK
PublisherIEEE
Pages3545-3552
Number of pages8
ISBN (Print)978-1-4244-1822-0
Publication statusPublished - 2008
Event9th IEEE Congress on Evolutionary Computation (CEC08) - Hong Kong, China
Duration: 1 Jun 2008 → …

Conference

Conference9th IEEE Congress on Evolutionary Computation (CEC08)
Country/TerritoryChina
CityHong Kong
Period1/06/08 → …

Keywords

  • SYSTEMS

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