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DOI: 10.4230/OASIcs.WCET.2008.1669

INFER: Interactive Timing Profiles based on Bayesian Networks

Authors: Michael Zolda

Published in: OASIcs, Volume 8, 8th International Workshop on Worst-Case Execution Time Analysis (WCET'08) (2008)

Abstract

We propose an approach for timing analysis of software-based embedded computer systems that builds on the established probabilistic framework of Bayesian networks. We envision an approach where we take (1) an abstract description of the control flow within a piece of software, and (2) a set of run-time traces, which are combined into a Bayesian network that can be seen as an interactive timing profile. The obtained profile can be used by the embedded systems engineer not only to obtain a probabilistic estimate of the WCET, but also to run interactive timing simulations, or to automatically identify software configurations that are likely to evoke noteworthy timing behavior, like, e.g., high variances of execution times, and which are therefore candidates for further inspection.

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Michael Zolda. INFER: Interactive Timing Profiles based on Bayesian Networks. In 8th International Workshop on Worst-Case Execution Time Analysis (WCET'08). Open Access Series in Informatics (OASIcs), Volume 8, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{zolda:OASIcs.WCET.2008.1669,
  author =	{Zolda, Michael},
  title =	{{INFER: Interactive Timing Profiles based on Bayesian Networks}},
  booktitle =	{8th International Workshop on Worst-Case Execution Time Analysis (WCET'08)},
  pages =	{1--13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-10-1},
  ISSN =	{2190-6807},
  year =	{2008},
  volume =	{8},
  editor =	{Kirner, Raimund},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.WCET.2008.1669},
  URN =		{urn:nbn:de:0030-drops-16695},
  doi =		{10.4230/OASIcs.WCET.2008.1669},
  annote =	{Keywords: Bayesian networks, embedded systems, hardware modeling, measurement-based execution time analysis, software modeling, probabilistic modeling, profilin}
}

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INFER: Interactive Timing Profiles based on Bayesian Networks

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