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.
Highlights
In the field of software reliability engineering, the quantitative assessment of software reliability has become one of the main issues of this area
It is well known in the software reliability engineering community that there does not exist a uniquely best parametric nonhomogeneous Poisson process (NHPP)-based software reliability models (SRMs) which can fit every type of software-fault count data
This paper focuses on the first step of wavelet shrinkage estimation (WSE) and aims at identifying and emphasizing the influence that the data transformation exerts on the accuracy of WSE
Summary
In the field of software reliability engineering, the quantitative assessment of software reliability has become one of the main issues of this area. It is well known in the software reliability engineering community that there does not exist a uniquely best parametric NHPP-based SRM which can fit every type of software-fault count data This fact implies that nonparametric methods without assuming parametric form should be used to describe the software debugging phenomenon which is different in each testing phase. Another class of non-parametric estimation methods for NHPP-based SRMs is the wavelet analysis-based approach, initiated by Xiao and Dohi [15] They proposed the wavelet shrinkage estimation (WSE), which does not require solving any optimization problem, so that the implementation of estimation algorithms is rather easy than the other nonparametric methods.
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