Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring software reusability design pattern based on To achieve this, authors proposed model five parameters, inheritance, number children, coupling, and complexity, measure factor using various soft computing techniques, Fuzzy, Neural Network, Neuro-Fuzzy. Further, also compared with four existing machine learning algorithms. Lastly, found neuro-fuzzy technique is trained well predicts MAE (Mean absolute error) 0.003 RMSE (Root mean square 0.009 Hence, it concluded better predictor than