Estimating Software Intensity Function Based on Translation-Invariant Poisson Smoothing Approach

Abstract

Because the software failure occurrence process is well-modeled by a non-homogeneous Poisson process, it is of great interest to estimate accurately the software intensity function of Non-Homogeneous Poisson Process (NHPP)-based software reliability models (SRM) from observed software-fault count data. In recent years, wavelet-based techniques have been well established in Poisson intensity estimation because of their technical advantages of computational cost and accuracy. The approach enables us to carry out the analysis of a software debugging process in a nonparametric way. In this paper, we propose an applied Haar wavelet-based approach which is without an approximate data transformation, for software reliability assessment. In a numerical study with real software-fault count data, we compare the proposed estimation method with the previously used data transformation-based estimation method, as well as conventional maximum likelihood estimation and least squares estimation methods. Furthermore, we conduct sensitivity analysis of the resolution level, which affects the estimation accuracy of the proposed method. We also estimate some predictive measures such as software reliability.