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        Using the support vector machine as a classification method for software defect prediction with static code metrics

        Author
        Gray, David
        Bowes, D.
        Davey, N.
        Sun, Yi
        Christianson, B.
        Attention
        2299/5864
        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.
        Publication date
        2009
        Published in
        Communications in Computer and Information Science
        Published version
        https://doi.org/10.1007/978-3-642-03969-0_21
        Other links
        http://hdl.handle.net/2299/5864
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