Determining Optimal Release and Testing Stop Time of a Software Using Discrete Approach

1. Introduction

Software has become the most important component in day to day activity as the technology is advancing rapidly. Firms put a lot of effort in order to develop free of faults software, thereby increasing the reliability of the software. The term software-reliability is defined as the probability of operating failure free in a given environment for some specified period of time (Pham 2006). There is a direct proportionality between the amount of testing time utilized during the development phase of the software and the amount of reliability achieved. However, we can’t spend a lot of time on testing as we have to produce the software within the stipulated time in the market. Further, during software development human errors may result in malfunctioning of the system. This is why a skilled testing team is required for developing a software system to build high quality software. This can be achieved through continuous software testing as it helps in removal faults causing failure of the software system. To help the testing team in planning the testing phase academicians and researchers developed a software reliability growth model (SRGM) to predict the number of faults in the system. These SRGM helps in predicting the software reliability at any point of time. Researchers utilized two types of SRGMs viz. calendar time and test case execution based SRGMs to predict the software reliability. The pioneer work in this direction is done by Goel and Okumoto (1979) to develop exponential distribution based SRGM. Post this researchers suggested that, sometimes the number of test runs or number of executed test cases are considered appropriate unit for fault detection period (Brooks and Motley,1980). Later, Yamada et al. (1983) developed a two stage SRGM by considering fault removal as two stage process i.e. fault detection and fault correction. Their SRGM followed S-shaped distribution and hence named as S-shaped SRGM. Yamada and Osaki (1985) proposed a discrete SRGM based on non-homogeneous Poisson process (NHPP) in which the random variable is defined as the number of faults detected by n test case runs. Kapur and Garg (1992) developed a flexible SRGM considering logistic distribution function. Researchers further developed calendar time and test case run based mathematical models by combining the cost incurred in testing and operational phase to solve another important problem of determining duration of the testing process (Okumoto and Goel,1980; Kapur et al.,1994).