Discrete time NHPP models for software reliability growth phenomenon

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