FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning

Fuente: arXiv
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Main Authors: Alshmrany, Kaled M., Aldughaim, Mohannad, Wei, Chenfeng, Sweet, Tom, Allmendinger, Richard, Cordeiro, Lucas C.
Format: Preprint
Published: 2024
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author Alshmrany, Kaled M.
Aldughaim, Mohannad
Wei, Chenfeng
Sweet, Tom
Allmendinger, Richard
Cordeiro, Lucas C.
author_facet Alshmrany, Kaled M.
Aldughaim, Mohannad
Wei, Chenfeng
Sweet, Tom
Allmendinger, Richard
Cordeiro, Lucas C.
contents We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.
format Preprint
id arxiv_https___arxiv_org_abs_2404_06031
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
Alshmrany, Kaled M.
Aldughaim, Mohannad
Wei, Chenfeng
Sweet, Tom
Allmendinger, Richard
Cordeiro, Lucas C.
Cryptography and Security
We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.
title FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
topic Cryptography and Security
url https://arxiv.org/abs/2404.06031