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Cross-project software aging defect prediction method

A software aging and prediction method technology, applied in software testing/debugging, computer components, error detection/correction, etc., can solve problems such as overfitting, large difference in prediction effect, and reduce migration effect, so as to avoid loss, The effect of improving prediction accuracy and strong robustness

Active Publication Date: 2020-11-03
WUHAN UNIV OF TECH
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Problems solved by technology

However, in the traditional method, only the difference of marginal distribution is considered, and the difference of conditional distribution is not considered, which reduces the transfer effect
And only using the oversampling method to deal with the class imbalance problem can easily lead to overfitting, which is not robust enough for different machine learning classifiers, that is, the prediction effect is quite different

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  • Cross-project software aging defect prediction method
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  • Cross-project software aging defect prediction method

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Embodiment Construction

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] Such as figure 1 As shown in the cross-project aging defect prediction flow diagram of the embodiment of the present invention, firstly, data preprocessing is performed on the source project and the target project, and then joint distribution domain adaptation (JDA) is used to reduce the marginal distribution and conditional distribution difference between the two projects. According to the characteristics of the aging data set, the improved subclass discriminant analysis method is adopted, and the undersampling method (RUS) is further used on the source items for processing. Finally, the machine learning classifier is used to classify the target item and output the prediction result.

[0020] The present invention is described in detail below in conjunction with example, the specific steps of a kind of new cross-project software aging predictio...

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Abstract

The invention discloses a cross-project software aging prediction method, which comprises the following steps of: preprocessing data in a source project and a target project, reducing edge distribution and condition distribution difference by adopting joint distribution domain adaptation, and relieving a class imbalance problem by adopting an undersampling method and an improved subclass discriminant analysis method; and finally, using a machine learning classifier (logistic regression and the like) to perform prediction. According to the method, the condition distribution difference between the source project and the target project of the software aging defect data set is considered, and an improved subclass discriminant analysis method and the like are further adopted to relieve the extremely serious class imbalance problem. The problem that a traditional cross-project software aging defect prediction method is not high in precision and robustness is solved, developers are helped tofind and remove software aging related defects in the development 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 processing, and in particular relates to a cross-project software aging defect prediction method. 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 scholars use code static features (s...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06K9/62G06N20/00
CPCG06F11/3672G06N20/00G06F18/24G06F18/214
Inventor 向剑文徐斌贾凯赵冬冬
Owner WUHAN UNIV OF TECH
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