A software defect tendency prediction method based on smote+boosting algorithm

Abstract

The invention discloses a SMOTE+Boosting algorithm based software defect tendency prediction method. The method comprises the steps of for the non-equilibrium problem of software defect data, firstly synthesizing artificial minority class samples by using an SMOTE algorithm and enabling the minority class samples and majority class samples to be balanced in quantity; secondly, calculating a penalty factor of each artificial minority class sample according to density information of original sample distribution to adjust a weight of the artificial sample so as to enable a basic classifier to distinguish the learning of an original sample and the artificial minority class sample and put more emphasis on the learning of the original sample and the artificial sample with relatively high credibility; continuing to use an original loss function for the original sample, adding a penalty factor for the loss function of the artificial minority class sample to penalize the artificial sample with low credibility, and determining a new loss function; and finally, forming a new Boosting algorithm. According to the method, the classification precision of a minority class and a majority class is improved and the problem of low classification precision of a classification model to the minority class caused by data non-equilibrium in the field of prediction and classification is solved to a certain extent.

Description

technical field [0001] The invention belongs to the technical field of software defect prediction, relates to software defect prediction technology in software quality prediction, and specifically refers to a software defect tendency prediction method based on SMOTE and Boosting algorithms. Background technique [0002] With the continuous development of computer technology and the wide application of software in people's production and life, people's requirements for software quality and reliability are getting higher and higher. People expect that by accurately predicting the software quality, it can be used to guide the resource allocation in the software development process, so as to ensure the delivery of software products on schedule and improve the quality of software products. [0003] At present, the prediction of software defects is to measure the quality of software by predicting the risk or number of defects contained in the software; using pattern recognition al...

AI Technical Summary

Problems solved by technology

[0018] The present invention proposes a method for predicting software defect tendency based on the SMOTE+Boosting algorithm in order to solve the problem of low classification accuracy of the minority class in the case of unbalanced data in the integrated algorithm, including the following steps:

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