A comparative study of data transformations for wavelet shrinkage estimation with application to software reliability assessment

  • Authors:

  • Xiao Xiao;Tadashi Dohi

  • Affiliations:

  • Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan;Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan

  • Venue:
  • Advances in Software Engineering - Special issue on Software Quality Assurance Methodologies and Techniques
  • Year:
  • 2012

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Abstract

In our previous work, we proposed wavelet shrinkage estimation (WSE) for nonhomogeneous Poisson process (NHPP)-based software reliability models (SRMs), where WSE is a data-transform-based nonparametric estimation method. Among many variance-stabilizing data transformations, the Anscombe transform and the Fisz transform were employed. We have shown that it could provide higher goodness-of-fit performance than the conventional maximum likelihood estimation (MLE) and the least squares estimation (LSE) in many cases, in spite of its non-parametric nature, through numerical experiments with real software-fault count data. With the aim of improving the estimation accuracy of WSE, in this paper we introduce other three data transformations to preprocess the software-fault count data and investigate the influence of different data transformations to the estimation accuracy of WSE through goodness-of-fit test.