Software quality prediction models are used to achieve high software reliability. Prediction models that estimate a quality factor for software modules can be used in directing corrective efforts. Precise quantitative prediction values for the quality factor is often not suffcient. Instead, predicting the rank-order of modules with respect to the quality factor may be more beneficial to the development team. A module-order model (MOM) uses an underlying quantitative prediction model to predict this rank-order.

This paper compares performances of module-order models of two different count models which are used as the underlying prediction models. They are the Poisson regression model (PRM) and the zero-inflated Poisson (ZIP) regression model. It is demonstrated that improving a count model for prediction does not ensure a better MOM performance. A case study of a full-scale industrial software system is used to compare performances of module-order models of the two count models. It was observe dthat improving prediction of the Poisson count model by using zero-inflated Poisson regression did not yield module-order models with better performance. Thus, it was concluded that the degree of prediction accuracy of the underlying model did not influence the results of the subsequent module-order model. Module-order modeling is proven to be a robust and effective method even though both underlying prediction may sometimes lack acceptable prediction accuracy.