Software defect prediction method for support vector machine based on neighborhood preserving embedding algorithm

Abstract

The invention provides a software defect prediction method for a support vector machine based on a neighborhood preserving embedding algorithm. The method is used for solving the problem of data redundancy of software metrics. The method comprises the steps that a training set X1 and a testing set X2 are selected from a software defect prediction data set; the NPE algorithm is adopted for conducting dimension reduction on the training set X1 and the testing set X2; a training set Y1 after dimension reduction is used as a training input set, the support vector machine (SVM) is used for training, and a trained defect prediction model is obtained; a testing set Y2 after dimension reduction is used as a testing input set, the trained defect prediction model is used for prediction, a predictionresult is compared with an actual result, and if the prediction result meets a stopping condition, the software defect prediction model at this time is an optimum software defect prediction model; ifnot, SVM training is executed again, and optimization is conducted.

Description

technical field [0001] The invention relates to a software defect prediction method based on a neighborhood embedding protection algorithm support vector machine, belonging to the technical field of software prediction. Background technique [0002] Software defects: IEEE729-1983 has a standard definition of defects. From the internal point of view of the product, defects are various problems such as errors and defects in the process of software product development or maintenance; from the external point of view of the product, defects are the software system and its original The failure or violation of a certain function that needs to be realized. [0003] Static prediction: Static software defect prediction technology is an earlier prediction technology that is currently researched and applied most. It is mainly based on extracting software-related measurement information to establish a corresponding prediction model for defect prediction. [0004] Prediction model based ...

AI Technical Summary

Problems solved by technology

[0012] The present invention proposes a software defect prediction method based on the support vector machine based on the neighborhood embedding protection algorithm, which is used to solve the problem of software measurement data redundancy, so that the final defect distribution prediction result has a higher accuracy than other traditional prediction technologies Accuracy

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