A neuro-fuzzy model for software cost estimation

Abstract

A novel neuro-fuzzy constructive cost model (COCOMO) for software estimation is proposed. The model carries some of the desirable features of the neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, this model is easily validated by experts and capable of generalization. In addition, it allows inputs to be continuous-rating values and linguistic values, therefore avoiding the problem of similar projects having different estimated costs. Also presented in this paper is a detailed learning algorithm. The validation, using industry project data, shows that the model greatly improves the estimation accuracy in comparison with the well-known COCOMO model.