Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en Bonenfant, Armelle; Claraz, Denis; de Michiel, Marianne; Sotin, Pascal http://www.dagstuhl.de/oasics License
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URN: urn:nbn:de:0030-drops-73073
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Early WCET Prediction Using Machine Learning

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Abstract

For delivering a precise Worst Case Execution Time (WCET), the WCET static analysers need the executable program and the target architecture. However, a prediction (even coarse) of the future WCET would be helpful at design stages where only the source code is available. We investigate the possibility of creating predictors of the WCET based on the C source code using machine-learning (work in progress). If successful, our proposal would offer to the designer precious information on the WCET of a piece of code at the early stages of the development process.

BibTeX - Entry

@InProceedings{bonenfant_et_al:OASIcs:2017:7307,
  author =	{Armelle Bonenfant and Denis Claraz and Marianne de Michiel and Pascal Sotin},
  title =	{{Early WCET Prediction Using Machine Learning}},
  booktitle =	{17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017)},
  pages =	{1--9},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-057-6},
  ISSN =	{2190-6807},
  year =	{2017},
  volume =	{57},
  editor =	{Jan Reineke},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7307},
  URN =		{urn:nbn:de:0030-drops-73073},
  doi =		{10.4230/OASIcs.WCET.2017.5},
  annote =	{Keywords: Early WCET, Machine Learning, Static Analysis, C Language}
}

Keywords: Early WCET, Machine Learning, Static Analysis, C Language
Seminar: 17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017)
Issue date: 2017
Date of publication: 2017


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