Research Article Open Access

An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction

Jie Xu, Danny Ho and Luiz F. Capretz

Abstract

Problem Statement: Object-oriented design has become a dominant method in software industry and many design metrics of object-oriented programs have been proposed for quality prediction, but there is no well-accepted statement on how significant those metrics are. In this study, empirical analysis is carried out to validate object-oriented design metrics for defects estimation. Approach: The Chidamber and Kemerer metrics suite is adopted to estimate the number of defects in the programs, which are extracted from a public NASA data set. The techniques involved are statistical analysis and neuro-fuzzy approach. Results: The results indicate that SLOC, WMC, CBO and RFC are reliable metrics for defect estimation. Overall, SLOC imposes most significant impact on the number of defects. Conclusions/Recommendations: The design metrics are closely related to the number of defects in OO classes, but we can not jump to a conclusion by using one analysis technique. We recommend using neuro-fuzzy approach together with statistical techniques to reveal the relationship between metrics and dependent variables, and the correlations among those metrics also have to be considered.

Journal of Computer Science
Volume 4 No. 7, 2008, 571-577

DOI: https://doi.org/10.3844/jcssp.2008.571.577

Submitted On: 25 September 2008 Published On: 31 July 2008

How to Cite: Xu, J., Ho, D. & Capretz, L. F. (2008). An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction . Journal of Computer Science, 4(7), 571-577. https://doi.org/10.3844/jcssp.2008.571.577

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Keywords

  • Software quality
  • design metrics
  • statistical analysis
  • neuro-fuzzy
  • prediction