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Yet Another Metric for Predicting Fault-Prone Modules

  • Conference paper
Advances in Software Engineering (ASEA 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 59))

Abstract

Recently, machine learning approaches have been widely used for fault-proneness detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To treat our approach as the conventional fault-prone approaches, we summarize the output of spam-filtering based approach as a metric. In this paper, we show the effectiveness of our new metric comparing the conventional software metrics.

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Author information

Authors and Affiliations

  1. Graduate School of Science and Technology, Kyoto Institute of Technology,  

    Osamu Mizuno

  2. Graduate School of Information Science and Technology, Osaka University,  

    Hideaki Hata

Authors
  1. Osamu Mizuno
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  2. Hideaki Hata
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Editor information

Editors and Affiliations

  1. Infobright Inc.,  

    Dominik Ślęzak

  2. Hannam University, 306-791, Daejeon, South Korea

    Tai-hoon Kim

  3. University of Michigan-Dearborn, Dearborn, MI, USA

    Akingbehin Kiumi

  4. University of California, Riverside, USA

    Tao Jiang

  5. National ICT, Australia

    June Verner

  6. Department of Information Systems and Computation, Valencia University of Technology, Camino de Vera s/n, P.O. Box: 22012, 46022, Valencia, Spain

    Silvia Abrahão

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© 2009 Springer-Verlag Berlin Heidelberg

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Mizuno, O., Hata, H. (2009). Yet Another Metric for Predicting Fault-Prone Modules. In: Ślęzak, D., Kim, Th., Kiumi, A., Jiang, T., Verner, J., Abrahão, S. (eds) Advances in Software Engineering. ASEA 2009. Communications in Computer and Information Science, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10619-4_36

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  • DOI: https://doi.org/10.1007/978-3-642-10619-4_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10618-7

  • Online ISBN: 978-3-642-10619-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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