New Directions in Worst-Case Execution Time analysis

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.