Software BUG classification method based on sparse cost matrix

Abstract

The invention discloses a software BUG classification method based on a sparse cost matrix. The method comprises the following steps of S1, obtaining a software BUG report; s2, encoding the software BUG report data; S3, initializing a sparse cost matrix; s4, training a weighted extreme learning machine by using the encoded data to enable the weighted extreme learning machine to output a correct report classification result; s5, solving a local generalization error of the weighted extreme learning machine by using the trained weighted extreme learning machine; s6, generating a new software BUGsparse weight matrix by using a crossover mutation strategy in a differential evolution algorithm; s7, training a new weighted extreme learning machine on the same unbalanced data set by using the newsparse weight matrix; S8, until a lower local generalization error cannot be obtained; and S9, predicting an unknown software BUG report by using a weighted extreme learning machine capable of obtaining the lowest local generalization error to obtain a corresponding report classification result.

Description

technical field [0001] The invention relates to software BUG classification, evolutionary algorithm, machine learning and local generalization error, in particular to a software BUG classification method based on sparse cost matrix for software BUG classification. Background technique [0002] Despite many advances in the field of software bug classification, existing work fails to achieve favorable performance when impacted by complex datasets with imbalanced forms. Unbalanced data problems are very common in many scenarios. Biased class distributions often lead to classification learning not being able to obtain the features of minority class samples. However, standard classification learning, including most extreme learning machine work, assumes two assumptions, equal misclassification costs and balanced class distribution, which often do not hold in practice. To address this issue, cost-sensitive and weighted extreme learning machines have been proposed for imbalanced ...

AI Technical Summary

Problems solved by technology

It is worth noting that it is crucial to set reasonable cost values ​​for different class samples, however, the rules of thumb do not apply in all cases, and an adaptive method for estimating these costs is urgently needed in this field

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