Software defect prediction method based on convolutional neural network

Abstract

The invention discloses a software defect prediction method based on a convolutional neural network. The software defect prediction method comprises the following steps that various source codes in asoftware project are parsed to form an AST Token vector set; mapping between integers and Token is established, and AST Token vectors are converted into value vectors; a SMOTE technology is utilized to dispose the problem of unbalanced classification of the data in a data vector set; the convolutional neural network is established based on the data vector set, and feature vectors capable of expressing code semantics are extracted; convolutional neural network learning characteristics and traditional manual static characteristics are merged; the data set having the merging characteristics is input into a support vector machine classifier, and a software defect prediction model is trained. The software defect prediction method can be directly applied to defect prediction tasks of practical software and can capture the semantic characteristics of source codes, the problem of semantic feature analysis missing in traditional methods is solved, and thus the accuracy of the defect predictionmodel is improved.

Description

technical field [0001] The invention relates to the field of software analysis and defect prediction in software engineering, in particular to a software defect prediction method based on a convolutional neural network. Background technique [0002] Potential and unknown defects in software will seriously affect the quality of software, so software analysis and defect prediction techniques play an important role in software quality assurance tasks. If software defects can be found early, it will help the software team understand the quality status of the current project, and then allocate testing resources reasonably. However, it is impractical to manually review all code units in a project. Therefore, more and more software engineering researchers and practitioners have begun to pay close attention to software defect prediction technology based on machine learning, and try to use a variety of machine learning methods to Detect potentially defective modules and files in sof...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a software defect prediction method based on convolutional neural network, which combines the technology of neural network automatic feature generation to solve the problem of traditional manual features in existing software defect prediction methods. The problem of missing semantic features

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