A defect code block positioning method based on model structure evolution

Abstract

This invention discloses a defective code block localization method based on model structure evolution. First, based on several deep learning computing framework libraries, its object libraries and security issues are summarized to construct a computing framework object library. Then, a computing framework security defect library and a code security defect library are constructed. Next, a neural network model is constructed and mutated to obtain a set of mutated models. Output differences are extracted from the mutated model set to obtain feature difference vectors, and the prediction matching rate is calculated to determine whether the neural network model contains defective code blocks. Finally, the defective code in the neural network model is verified and traced using statement manipulation methods, and compared with the computing framework security defect library and the code security defect library to achieve defective code block localization. This invention reduces computational costs, has wider deployment and application scenarios, and improves the accuracy of code block defect localization.

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