A Comprehensive Empirical Study of Count Models for Software Fault Prediction

Count models, such as the Poisson regression model, and the negative binomial regression model, can be used to obtain software fault predictions. With the aid of such predictions, the development team can improve the quality of operational software. The zero-inflated, and hurdle count models may be more appropriate when, for a given software system, the number of modules with faults are very few. Related literature lacks quantitative guidance regarding the application of count models for software quality prediction. This study presents a comprehensive empirical investigation of eight count models in the context of software fault prediction. It includes comparative hypothesis testing, model selection, and performance evaluation for the count models with respect to different criteria.