How effective are they? Exploring large language model based fuzz driver generation

How effective are they? Exploring large language model based fuzz driver generation

Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of resear...

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Bibliographic Details
Main Authors: ZHANG, Cen, ZHENG, Yaowen, BAI, Mingqiang, LI, Yeting, MA, Wei, XIE, Xiaofei
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Fuzz driver generation
Fuzz testing
Large language model
Artificial Intelligence and Robotics
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/9508
https://ink.library.smu.edu.sg/context/sis_research/article/10508/viewcontent/How_Effective_Are_They__Exploring_Large_Language_Model_Based_Fuzz_Driver_Generation.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9508
https://ink.library.smu.edu.sg/context/sis_research/article/10508/viewcontent/How_Effective_Are_They__Exploring_Large_Language_Model_Based_Fuzz_Driver_Generation.pdf

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