Discrete Time NHPP Models for Software Reliability Growth Phenomenon

Nonhomogeneous poisson process based software reliability growth models are generally classified into twogroups. The first group contains models, which use the machine execution time or calendar time as a unit of faultdetection/removal period. Such models are called continuous time models. The second group contains models, which use thenumber of test occasions/cases as a unit of fault detection period. Such models are called discrete time models, since the unitof software fault detection period is countable. A large number of models have been developed in the first group while thereare fewer in the second group. Discrete time models in software reliability are important and a little effort has been made inthis direction. In this paper, we develop two discrete time SRGMs using probability generating function for the softwarefailure occurrence / fault detection phenomenon based on a NHPP namely, basic and extended models. The basic modelexploits the fault detection/removal rate during the initial and final test cases. Whereas, the extended model incorporates faultgeneration and imperfect debugging with learning. Actual software reliability data have been used to demonstrate theproposed models. The results are fairly encouraging in terms of goodness-of-fit and predictive validity criteria due toapplicability and flexibility of the proposed models as they can capture a wide class of reliability growth curves ranging frompurely exponential to highly S-shaped