FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910403195305984 |
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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 |