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- W150336312 abstract "The automated detection of defective modules within software systems could lead to reduced development costs and more reliable software. In this work the static code metrics for a collection of modules contained within eleven NASA data sets are used with a Support Vector Machine classifier. A rigorous sequence of pre-processing steps were applied to the data prior to classification, including the balancing of both classes (defective or otherwise) and the removal of a large number of repeating instances. The Support Vector Machine in this experiment yields an average accuracy of 70% on previously unseen data." @default.
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- W150336312 date "2009-01-01" @default.
- W150336312 modified "2023-10-16" @default.
- W150336312 title "Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics" @default.
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- W150336312 doi "https://doi.org/10.1007/978-3-642-03969-0_21" @default.
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