License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.WCET.2012.103
URN: urn:nbn:de:0030-drops-35611
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2012/3561/
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Marref, Amine

Evolutionary Techniques for Parametric WCET Analysis

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p103-marref.pdf (0.4 MB)

Abstract

Estimating the worst-case execution time (WCET) of real-time programs is pivotal in their verification. WCET estimation either yields a numeric value that represents the maximum execution time of the program when executed on a specific hardware platform; or yields a parametric expression in the form of some function of the input which when instantiated with a particular input value, gives a WCET estimation of the program when triggered by this input specifically
(on a specific hardware platform). Parametric WCET analysis provides extra accuracy as the WCET estimation can be tuned to particular input values at runtime; and is usually of interest to dynamic-scheduling schemes.
In this paper we use genetic programming as an alternative method to approach the problem of parametric WCET analysis. Parametric expressions are captured automatically by the genetic program based on end-to-end executions of the program under analysis. The technique
is complementary to static parametric WCET analysis and more amenable to industrial practice. Experimental evaluation shows that the presented technique computes accurate parametric expression in an almost negligible time.

BibTeX - Entry

@InProceedings{marref:OASIcs:2012:3561,
  author =	{Amine Marref},
  title =	{{Evolutionary Techniques for Parametric WCET Analysis}},
  booktitle =	{12th International Workshop on Worst-Case Execution Time Analysis},
  pages =	{103--115},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-41-5},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{23},
  editor =	{Tullio Vardanega},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2012/3561},
  URN =		{urn:nbn:de:0030-drops-35611},
  doi =		{10.4230/OASIcs.WCET.2012.103},
  annote =	{Keywords: Real-time systems, parametric worst-case execution-time analysis, end- to-end testing, genetic programming}
}

Keywords: Real-time systems, parametric worst-case execution-time analysis, end- to-end testing, genetic programming
Collection: 12th International Workshop on Worst-Case Execution Time Analysis
Issue Date: 2012
Date of publication: 10.07.2012

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