Python library fuzzy test drive generation method based on large language model

Abstract

The invention focuses on the field of fuzz testing, and particularly relates to intelligent auxiliary fuzz testing drive code generation by using a large language model. The invention provides a convenient, fast and automatic fuzz test driving code generation method for Python library code fuzz test by using a large language model and combining a test function and a sample code. The method comprises the steps that Python library source code analysis and drive code sample information extraction are conducted through a static analysis tool, and a data set of a test function and a sample code is obtained; constructing a prompt word engineering structure comprising a drive sample code, a generation task description, a variation strategy introduction, a test function information set, a drive sample code and a code standardization description, and generating zero-sample, less-sample and thinking chain prompts according to the number of samples; building a large language model agent end, transmitting cue words to open-source and closed-source large language models in an HTTP / HTTPS (Hyper Text Transfer Protocol / Hyper Text Transfer Protocol Secure Socket) message mode, and performing reasoning generation of fuzzy test driving codes; after the drive codes are obtained, the fuzz test drive codes which do not pass the pilot run test are generated and screened for multiple rounds, and finally a fuzz test drive code set to be subjected to vulnerability mining is obtained; and performing vulnerability mining on the test function by utilizing the obtained fuzzy test driving code set, and storing a result to a database for analysis.