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.
@article{arxiv.2404.06031,
title = {FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning},
author = {Kaled M. Alshmrany and Mohannad Aldughaim and Chenfeng Wei and Tom Sweet and Richard Allmendinger and Lucas C. Cordeiro},
journal= {arXiv preprint arXiv:2404.06031},
year = {2024}
}