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- W2965699074 abstract "Software fault prediction (SFP) is an active issue in software engineering (SE). At present, machine learning (ML) has been successfully applied to SFP classification problems. However, one of the challenges for building software fault prediction models (SFPM) is processing high dimensional datasets, which include many irrelevant and redundant features. To address this issue, feature selection techniques, mainly contain wrapper methods and filter methods, are used. In the paper, we report an empirical study aimed at providing a novel approach to select feature for SFP." @default.
- W2965699074 created "2019-08-13" @default.
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- W2965699074 date "2019-01-01" @default.
- W2965699074 modified "2023-09-24" @default.
- W2965699074 title "A Novel Feature Selection Method for Software Fault Prediction Model" @default.
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- W2965699074 doi "https://doi.org/10.1109/rams.2019.8768923" @default.
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