Parametric Bootstrapping for Assessing Software Reliability Measures

Abstract

The bootstrapping is a statistical technique to replicate the underlying data based on the resampling, and enables us to investigate the statistical properties. It is useful to estimate standard errors and confidence intervals for complex estimators of complex parameters of the probability distribution from a small number of data. In software reliability engineering, it is common to estimate software reliability measures from the fault data (fault-detection time data) and to focus on only the point estimation. However, it is difficult in general to carry out the interval estimation or to obtain the probability distributions of the associated estimators, without applying any approximate method. In this paper, we assume that the software fault-detection process in the system testing is described by a non-homogeneous Poisson process, and develop a comprehensive technique to study the probability distributions on significant software reliability measures. Based on the maximum likelihood estimation, we assess the probability distributions of estimators such as the initial number of software faults remaining in the software, software intensity function, mean value function and software reliability function, via parametric bootstrapping method.