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Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics

  • Conference paper
Engineering Applications of Neural Networks (EANN 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 43))

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.

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Author information

Authors and Affiliations

  1. Science and Technology Research Institute, University of Hertfordshire, UK

    David Gray, David Bowes, Neil Davey, Yi Sun & Bruce Christianson

Authors
  1. David Gray
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  2. David Bowes
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  3. Neil Davey
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  4. Yi Sun
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  5. Bruce Christianson
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Editor information

Editors and Affiliations

  1. Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, N7 8DB, London, UK

    Dominic Palmer-Brown

  2. School of Computing, IT and Engineering, University of East London, Docklands Campus, 4-6 University Way, E16 2RD, London, UK

    Chrisina Draganova  & Haris Mouratidis  & 

  3. School of Computing, IT and Engineering, University of East London, London, UK

    Elias Pimenidis

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© 2009 Springer-Verlag Berlin Heidelberg

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Gray, D., Bowes, D., Davey, N., Sun, Y., Christianson, B. (2009). Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_21

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  • DOI: https://doi.org/10.1007/978-3-642-03969-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03968-3

  • Online ISBN: 978-3-642-03969-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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