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- W2964282359 abstract "Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach." @default.
- W2964282359 created "2019-07-30" @default.
- W2964282359 creator A5083747207 @default.
- W2964282359 date "2019-07-01" @default.
- W2964282359 modified "2023-10-02" @default.
- W2964282359 title "Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier" @default.
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- W2964282359 doi "https://doi.org/10.4018/ijsda.2019070103" @default.
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