Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

• Main Authors: Bhatia, P.K (Author), Duhan, M. (Author)
• Format: Article
• Language: English
• Published: Research Institute of Intelligent Computer Systems 2022
• Subjects: Dynamic metrics; Dynamic polymorphism; Fuzzy system; Neural network model; Neuro-fuzzy system; Software reusability
• Online Access: View Fulltext in Publisher

 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 the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than © 2022. International Journal of Computing.All Rights Reserved.