There are many techniques for performing software reliability modeling. In the environment of software development some models use the stochastic nature of fault introduction and fault removal to predict reliability. This thesis research analyzes a stochastic approach to software reliability modeling and its performance on two distinct software development lifecycles. The derivation of the model is applied to each lifecycle. Contrasts between the lifecycles are shown.

Actual data collected from industry projects illustrate the performance of the model to the lifecycle. Actual software development fault data is used in select phases of each lifecycle for comparisons with the model predicted fault data. Various enhancements to the model are presented and evaluated, including optimization of the parameters based on partial observations.