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

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  • D. Gray
  • D. Bowes
  • N. Davey
  • Y. Sun
  • B. Christianson
Publication date2009
Host publicationInternational Conference on Engineering Applications of Neural Networks EANN 2009: Engineering Applications of Neural Networks
Number of pages12
ISBN (Electronic)9783642039690
ISBN (Print)9783642039683
<mark>Original language</mark>English

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


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.