Modeling on Generalized Extended Inverse Weibull Software Reliability Growth Model

David D Hanagal,Nileema N Bhalerao

doi:10.6339/jds.201907_17(3).0007

Abstract

In this paper we introduce the generalized extended inverse Weibull finite failure software reliability growth model which includes both increasing/decreasing nature of the hazard function. The increasing/decreasing behavior of failure occurrence rate fault is taken into account by the hazard of the generalized extended inverse Weibull distribution. We proposed a finite failure non-homogeneous Poisson process (NHPP) software reliability growth model and obtain unknown model parameters using the maximum likelihood method for interval domain data. Illustrations have been given to estimate the parameters using standard data sets taken from actual software projects. A goodness of fit test is performed to check statistically whether the fitted model provides a good fit with the observed data. We discuss the goodness of fit test based on the Kolmogorov-Smirnov (K-S) test statistic. The proposed model is compared with some of the standard existing models through error sum of squares, mean sum of squares, predictive ratio risk and Akaikes information criteria using three different data sets. We show that the observed data fits the proposed software reliability growth model. We also show that the proposed model performs satisfactory better than the existing finite failure category models

Highlights

Due to the rapid development of computer and information technology, society increasingly depends on software-intensive systems
The purpose of this paper is to introduce a generalized extended inverse Weibull software reliability model which includes both increasing/decreasing nature of the hazard function
The remainder of the paper is organized as follows: In Section 2, we describe a finite failure non-homogeneous Poisson process (NHPP) class of software reliability growth model (SRGM), and offer a decomposition of mean value function (MVF) to the finite failure NHPP models which enables us to relate the nature of failure intensity of the software to the hazard function and examine the suitability of some finite failure models

Summary

Introduction

Due to the rapid development of computer and information technology, society increasingly depends on software-intensive systems. Most of the existing finite failure category models describe the failure rate either a constant, increasing or decreasing over time, for further information regarding this, one can refer Lyu (1996) and Pham (2006) In reality this may not be true because in early stage of testing, the testers are new to the software and they need time to adjust. The NHPP model is based on the following assumptions: 1. The failure has an independent increment i.e. the number of failure during the time interval (t, t + s) depends on the current time t and the length of the time interval s, and does not depend on the past history of the process

The failure rate of the process is given by

Some Existing NHPP Models

Generalized Extended Inverse Weibull Finite Failure NHPP Model

Analysis of Three Data Sets

Conclusions and Remarks