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Integrated learning-based software defect reopening prediction method

A software defect and integrated learning technology, applied in software testing/debugging, hardware monitoring, error detection/correction, etc., can solve problems such as data imbalance and unsatisfactory prediction results

Inactive Publication Date: 2017-08-18
XI AN JIAOTONG UNIV
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  • Application Information

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Problems solved by technology

[0005] The object of the present invention is to provide a software defect re-opening defect prediction method based on sampling and integrated learning, to overcome the defects described in the background technology above, the present invention can solve the prediction caused by data imbalance in the software defect re-opening prediction Unsatisfactory effect

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  • Integrated learning-based software defect reopening prediction method
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  • Integrated learning-based software defect reopening prediction method

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Embodiment Construction

[0048] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0049] Such as Figure 1 to Figure 6 As shown, the present invention aims at the extreme imbalance of the software defect reopening data set and the limitation of the feature set used in the current software defect reopening prediction, adopts the defect reopening prediction method based on UnderSMOTE sampling and ensemble learning, and is divided into data There are three processes of extraction, model training and prediction.

[0050] The input of the data extraction process is the software defect report corresponding to the software and the git library of the development control version, and the output is the extracted classification instance set that can be used for training the model. The present invention obtains the feature set in Table 1 by crawling, organizing and analyzing the software defect report in the online database of the software defect management s...

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Abstract

The invention discloses an integrated learning-based software defect reopening prediction method. The method comprises the following steps of: S1, extracting an LWEparagraph2vec-based semantic vector features from a defect report of software; S2, combining the LWEparagraph2vec-based semantic vector features extracted from the defect report of the software with meta features thereof to form a feature set; S3, constructing a prediction model according to an imbalanced data processing-based integrated learning prediction algorithm UnderSMOTEBagging method; and S4, obtaining a class label of a living example according to the feature set extracted in the step S2 and the prediction model obtained in the step S2, so as to judge whether detects of the software is going to be reopened or not. The method disclosed by the invention is capable of solving the problem that the prediction effect is not ideal due to data imbalance in software defect reopening prediction and finiteness of the used feature set.

Description

technical field [0001] The invention belongs to the technical field of software security, and in particular relates to a software defect re-opening prediction method based on semantic features and integrated learning. Background technique [0002] As telecommunications, national defense, commerce, finance, transportation, medical care and other industries continue to develop towards informatization and intelligence, large-scale software systems have gradually become an inseparable part of most people's daily lives. Most of the cost of these software systems is for the maintenance of these software systems. In fact, previous research has shown that more than 90% of software development costs are spent on software maintenance and evolution activities. [0003] In the process of software development and maintenance, software defect repair is one of the key activities. The vast majority of open source and commercial software projects use software bug tracking systems, such as ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06F11/34
CPCG06F11/3447G06F11/3476G06F11/3604
Inventor 朱晓燕曹振华王羽杨晓梅程龙
Owner XI AN JIAOTONG UNIV
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