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Early WCET Prediction using Machine Learning

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.
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https://hal.archives-ouvertes.fr/hal-03116285
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Submitted on : Friday, January 22, 2021 - 2:08:01 PM
Last modification on : Wednesday, November 3, 2021 - 7:16:55 AM
Long-term archiving on: : Friday, April 23, 2021 - 7:02:57 PM

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Armelle Bonenfant, Denis Claraz, Marianne de Michiel, Pascal Sotin. Early WCET Prediction using Machine Learning. 17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017), Jun 2017, Dubrovnik, Croatia. pp.5:1 - 5:9, ⟨10.4230/OASIcs.WCET.2017.5⟩. ⟨hal-03116285⟩

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