Software defect prediction method based on feature set division and ensemble learning

Abstract

The invention discloses a software defect prediction method based on feature set division and ensemble learning, and the method comprises the steps: dividing an original data set into a training dataset and a test data set, and dividing the training data set into a plurality of feature subsets; selecting K base classifiers for ensemble learning, and synthesizing an ensemble classifier of each feature subset according to the base classifiers and the corresponding weights; selecting a feature subset most similar to the input instance, performing defect prediction on the input instance by usingan integrated classifier of the feature subset, and establishing a software defect prediction model; dividing the test data set and searching a feature subset most similar to the input instance; searching optimal values of the centroid set and the weight set, and optimizing the software defect prediction model by combining the most similar feature subset of the test data set. The method has the advantages that redundant features in the defect prediction data set can be removed, the search space of the algorithm is reduced, and the problem of high feature dimension of historical data of software defects can be effectively relieved.

Description

technical field [0001] The invention relates to the technical field of software defect prediction, in particular to a software defect prediction method based on feature set division and integrated learning. Background technique [0002] The purpose of software defect prediction is mainly to distinguish software modules as defective modules or non-defective modules by using related technologies through historical software defect information, so software defect prediction is essentially a binary classification problem. Defective modules can be effectively identified through defect prediction, thereby reducing various risks and hazards caused by software defects. At present, many machine learning algorithms have been used to build predictive models. For example, the classification rules generated by the decision tree C4.5 algorithm are easy to understand and have a fast learning speed, and are often used as benchmark comparison algorithms for model building; Naive Bayesian algo...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a software defect prediction method based on feature set division and integrated learning. In addition to the redundant features in the defect prediction data set, reducing the search space of the algorithm can also effectively alleviate the problem of high dimensionality of software defect historical data features; on the other hand, this method uses ensemble learning technology to integrate the classification results of different base classifiers, which can Effectively overcome the problem of low prediction accuracy of defective modules caused by data set imbalance

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