Multi-feature software defect comprehensive prediction method based on unbalanced noise set

Abstract

The invention discloses a multi-feature software defect comprehensive prediction method based on an unbalanced noise set. The multi-feature software defect comprehensive prediction method comprises the following steps: constructing an initial data set containing code features, development process features and network structure features; performing preliminary undersampling processing on the data set, and reducing repeated data in most classes; searching a k nearest neighbor sample set for metric elements in the data set through a tendency score matching method; realizing noise reduction processing of the data set through k nearest neighbor sample threshold judgment; performing sample synthesis on the minority class in the data set and the minority class in the k nearest neighbor sample setto eliminate the class imbalance problem of the data set; and adaptively constructing a plurality of machine learning models and selecting the most suitable machine learning model to perform defect prediction on the new version of software. According to the method, the problem of class imbalance generally existing in software defect prediction is solved. And noise samples are removed based on noise discrimination processing of tendency score matching.

Description

technical field [0001] The invention relates to the technical fields of software defect prediction and software reliability, in particular to a comprehensive prediction method for multi-feature software defects based on an unbalanced noise set. Background technique [0002] Since the 21st century, the status of software in social life has been increasing, and it is ubiquitous. It is not only limited to professional fields such as engineering, scientific research, and economics, but has also entered thousands of households and has become an important tool in people's lives. With the increasing scale and complexity of software systems, software defects are also increasing day by day, and software defects have also affected all aspects of people's lives. [0003] Software defect data is the basis of research and application related to software reliability. For a long time, the defect data used in the field of software reliability mainly comes from limited software testing data...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to propose a comprehensive prediction method for multi-feature software defects based on an unbalanced noise set, which can solve the problems caused by incomplete feature measurement, high degree of unbalanced data sets, and wrong labels in the data set in the prior art. noise samples etc.

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