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International Conference on Enterprise Information Systems

ICEIS 2013: Enterprise Information Systems pp 209–225Cite as

Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 190))

Abstract

Logistic regression (LR) and naïve Bayes (NB) extensively used for prediction of fault-proneness assume linear addition and independence that often cannot hold in practice. Hence, we propose a Bayesian network (BN) model with incorporation of data mining techniques as an integrative approach. Compared with LR and NB, BN provides a flexible modeling framework, thus avoiding the corresponding assumptions. Using the static metrics such as Chidamber and Kemerer’s (C-K) suite and complexity as predictors, the differences in performance between LR, NB and BN models were examined for fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript, developed using the agile process. By cross validation and independent test of continual versions, we conclude that the proposed BN can achieve a better prediction than LR and NB for the agile software due to its flexible modeling framework and incorporation of multiple sophisticated learning algorithms.

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Acknowledgements

This research is partly supported by the Hong Kong CERG grant PolyU5225/08E, NSFC grant 1171344/D010703, MOST grants (2012CB955503 and 2011AA120305–1).

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Authors and Affiliations

  1. LREIS, Institute of Geographical Sciences and Resources Research, CAS, Beijing, China

    Lianfa Li

  2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Lianfa Li & Hareton Leung

Authors
  1. Lianfa Li
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  2. Hareton Leung
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Corresponding author

Correspondence to Hareton Leung .

Editor information

Editors and Affiliations

  1. Groupe ESEO, Angers, France

    Slimane Hammoudi

  2. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Portugal

    José Cordeiro

  3. Wroclaw University of Economics, Wroclaw, Poland & Macquarie University, Sydney, NSW, Australia

    Leszek A. Maciaszek

  4. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Poland

    Joaquim Filipe

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Li, L., Leung, H. (2014). Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System. In: Hammoudi, S., Cordeiro, J., Maciaszek, L., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2013. Lecture Notes in Business Information Processing, vol 190. Springer, Cham. https://doi.org/10.1007/978-3-319-09492-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-09492-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09491-5

  • Online ISBN: 978-3-319-09492-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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