Boosting based cost sensitive software defect prediction method

Abstract

The invention discloses a boosting based cost sensitive software defect prediction method, belonging to the technical field of software engineering application. According to the invention, re-sampling is carried out by means of Bootstrap, the erroneous deletion of valuable attributes can be prevented by using a cost-sensitive subclass selection manner of randomly deleting an attribute during attribute selection, and meanwhile, the selected attribute sub-class facilitates the reduction of prediction error costs, when the weight is updated, a cost-sensitive weight updating mechanism is adopted, large weight is endowed to a data set with high cost, so that the data can be guaranteed to be studied for many times to obtain a reasonable integrated prediction model, and the prediction model is applied to small sample data to accurately predict software defects, and therefore, the technical problems that the prediction effects are not satisfactory due to the shortage of training data under the small sample data and the unequal cost of false positives and false negatives in the prediction process are solved.

Description

technical field [0001] The invention discloses a cost-sensitive software defect prediction method based on Boosting, which belongs to the technical field of software engineering applications. Background technique [0002] At present, the classical learning methods used in the field of machine learning for static methods of software defect prediction mainly include naive Bayesian, support vector machines, decision trees, BP neural networks, random forests, or improved methods based on these methods. However, due to these learning Some algorithms have strict restrictions on the tested data, and some processing data is not accurate enough, which leads to unsatisfactory results when applied to the prediction of software defects. Moreover, in the case of small samples, all the above learning methods do not comprehensively consider a series of problems such as insufficient training data, class imbalance of training results, false positives and false negatives in the prediction pro...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned background technology, and to provide a cost-sensitive software defect prediction method based on Boosting, which adopts a combination of Boosting and cost-sensitive software in the process of attribute subset selection and weight update process Both have added cost-sensitive ideas to realize accurate prediction of software defects under small sample data, and solve the technical problems of insufficient training data under small sample data, false positives and false negatives during the prediction process, resulting in unsatisfactory prediction results

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