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International Conference on Advanced Computer Science and Information Technology

International Conference on Advanced Communication and Networking

AST 2010, ACN 2010: Advances in Computer Science and Information Technology pp 457–468Cite as

An Integrated Approach to Detect Fault-Prone Modules Using Complexity and Text Feature Metrics

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6059))

Abstract

Early detection of fault-prone products is necessary to assure the quality of software product. Therefore, fault-prone module detection is one of the major and traditional area of software engineering. Although there are many approaches to detect fault-prone modules, they have their own pros and cons. Consequently, it is recommended to use appropriate approach on the various situations. This paper tries to show an integrated approach using two different fault-prone module detection approaches.

To do so, we prepare two approaches of fault-prone module detection: a text feature metrics based approach using naive Bayes classifier and a complexity metrics based approach using logistic regression. The former one is proposed by us and the latter one is widely used approach. For the data for application, we used data obtained from Eclipse, which is publicly available.

From the result of pre-experiment, we find that each approach has the pros and cons. That is, the text feature based approach has high recall, and complexity metrics based approach has high precision. In order to use their merits effectively, we proposed an integrated approach to apply these two approaches for fault-prone module detection. The result of experiment shows that the proposed approach shows better accuracy than each approach.

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Author information

Authors and Affiliations

  1. Kyoto Institute of Technology, Matsugasaki GoshoKaido-cho, Sakyo-ku, Kyoto, 606-8585, Japan

    Osamu Mizuno

  2. Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Hideaki Hata

Authors
  1. Osamu Mizuno
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  2. Hideaki Hata
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Editor information

Editors and Affiliations

  1. Hannam University, 133 Ojeong-dong, daeduk-gu, 306-791, Daejeon, South Korea

    Tai-hoon Kim

  2. The Ohio State University, 43210, Columbus, OH, USA

    Hojjat Adeli

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Mizuno, O., Hata, H. (2010). An Integrated Approach to Detect Fault-Prone Modules Using Complexity and Text Feature Metrics. In: Kim, Th., Adeli, H. (eds) Advances in Computer Science and Information Technology. AST ACN 2010 2010. Lecture Notes in Computer Science, vol 6059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13577-4_41

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  • DOI: https://doi.org/10.1007/978-3-642-13577-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13576-7

  • Online ISBN: 978-3-642-13577-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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