Model misspecification effects within a family of alternative discrete reliability-growth models

We describe a family of related discrete reliability-growth methodologies potentially applicable to one-shot systems undergoing a test-analyze-and-fix development process. The common feature shared by the models is their connection to Duane's renowned learning-curve property. The major difference, however, lies in their applicability in the context of two intrinsically different sampling schemes. For each model, a summary of the statistical properties of various estimators of the parameters as well as the reliability of the system, are reported. For purposes of assessing model misspecification, a particular text execution scenario conforming to a inverse sampling scheme is adopted. In reliability applications, it is not an uncommon practice to borrow inference results from models which are inappropriate in this setting. A detailed study of the potential impact of such misspecification on the estimation of system reliability is presented.