2026-08-16T09:04:27Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7307 2024-03-06T10:30:08Z ddc:004 open_access

Early WCET Prediction Using Machine Learning Bonenfant, Armelle Claraz, Denis de Michiel, Marianne Sotin, Pascal Early WCET Machine Learning Static Analysis C Language 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Armelle Bonenfant and Denis Claraz and Marianne de Michiel and Pascal Sotin 2017 Is Part Of OASIcs, Volume 57, 17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.WCET.2017.5 urn:nbn:de:0030-drops-73073 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.WCET.2017.5 eng https://creativecommons.org/licenses/by/3.0/legalcode