FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning

Authors: Kaled M. Alshmrany, Mohannad Aldughaim, Chenfeng Wei, Tom Sweet, Richard Allmendinger, Lucas C. Cordeiro
Published: 2024-04-09

Source: https://arxiv.org/abs/2404.06031

PDF: https://arxiv.org/pdf/2404.06031

Labels Predicted by AI

テスト優先順位付け プログラム解析 リソース最適化

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

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.