Title :
New Directions in Worst-Case Execution Time analysis
Author :
Bate, Iain ; Kazakov, Dimitar
Author_Institution :
Dept. of Comput. Sci., Univ. of York, York
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.
Keywords :
learning (artificial intelligence); program compilers; program diagnostics; program testing; real-time systems; branch prediction; loop bounds; machine learning; software testing; worst-case execution time analysis; Genetic algorithms; History; Information analysis; Machine learning; Motion measurement; Predictive models; Software engineering; Software testing; Time measurement; Timing;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631277
