Method for predicting aging defects of software in project based on Active Learning

Abstract

The invention discloses an in-project software aging prediction method based on Active Learning, and the method comprises the steps: collecting the static measurement of a code in software, selectinga sample through Active Learning, carrying out the labeling of the sample, taking the sample as a training set, and predicting the remaining samples without class labels; and adopting active Learningfor sample selection and manual labeling, and forming a training set. An oversampling and undersampling combined method is adopted to relieve the class imbalance problem, and a machine learning classifier is used for prediction. According to the method, few software aging defect data set samples are considered, time and labor are consumed for collection, the problem of polar imbalance is relievedby adopting an undersampling and oversampling combined method, developers are helped to discover and remove software aging related defects in the development and test stage, and losses caused by the software aging problem are avoided. The feasibility of the method is verified on real software, and the method can be popularized to other software to predict software aging related defects.

Description

technical field [0001] The invention belongs to the technical field of software aging prediction, and in particular relates to a software aging defect prediction method in a project based on Active Learning. Background technique [0002] In long-running operating systems, software aging is the main cause of system performance degradation or software crashes. It is caused by Aging-Related Bugs (ARB), such as memory leaks, unreleased file locks, storage problems, etc. And it has been found to exist in various systems, such as Android, Linux, Windows, etc. The complexity and time characteristics of software aging make its detection very difficult. Therefore, predicting and removing software aging-related defects in the development and testing phase (code level) is one of the important ways to reduce the losses caused by software aging. [0003] In recent years, aging defect prediction has gradually attracted the attention of scholars in the field of reliability. Some schola...

AI Technical Summary

Problems solved by technology

Although the amount of data in this method is sufficient, the differences between different projects are still relatively serious. Therefore, there are still certain differences between the prediction performance across projects and the prediction performance within projects.

Moreover, in previous studies, when dealing with extremely serious class imbalances, a single method of oversampling or undersampling was used, which can easily lead to overfitting and is not robust enough for different machine learning classifiers, that is, the prediction effect is quite different.

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