Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models’ inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of ?t (adaptability) and predictive accuracy of the evolved model is measured using ?ve different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically signi?cant goodness of ?t and predictive accuracy