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In the authors' previous work, they proposed Wavelet Shrinkage Estimation (WSE) for Non-Homogeneous Poisson Process (NHPP)-based Software Reliability Models (SRMs), where WSE is a data-transform-based non-parametric estimation method. Among many variance-stabilizing data transformations, the Anscombe transform and the Fisz transform were employed. They 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 they 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.
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