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

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 tra...

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Bibliographic Details
Main Authors ZHAO DONGDONG, LI LIN, LI DIMENG, LIANG MENGTING, HU WENHUA, XIANG JIANWEN
Format Patent
LanguageChinese
English
Published 19.03.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary: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 a
Bibliography:Application Number: CN202011511241