New Directions in Worst-Case Execution Time Analysis

被引：0
|
作者：
Bate, Iain [1 ]
Kazakov, Dimitar [1 ]
机构：
[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England
来源：
2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/CEC.2008.4631277
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：3545 / 3552
页数：8
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