A Model Recommender System
with Efficient Taxonomy of Software Reusable Components
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The contribution of the Hybrid Optimization driven RideNN for Software Reusability Estimation is that the C-RideNN algorithm uses the current Cat Swarm Optimization (CSO) together with the Rider Neural Network (RideNN) for training purposes. This approach consists of developing a technique for the software reuse prediction model to maintain optimal reuse of software components without the likelihood of aging and prone to failure. Criteria, such as complexity, cohesion, and coupling, are considered for reuse with a total of nine metrics. Estimation is performed with the proposed neural network ...
The contribution of the Hybrid Optimization driven RideNN for Software Reusability Estimation is that the C-RideNN algorithm uses the current Cat Swarm Optimization (CSO) together with the Rider Neural Network (RideNN) for training purposes. This approach consists of developing a technique for the software reuse prediction model to maintain optimal reuse of software components without the likelihood of aging and prone to failure. Criteria, such as complexity, cohesion, and coupling, are considered for reuse with a total of nine metrics. Estimation is performed with the proposed neural network algorithm based on Cat Swarm Rider (C-RideNN) optimization. The The C-RideNN algorithm is formulated by integrating the CSO with the ROA algorithm Estimating software reuse using NN-based optimization has been shown to produce an improved total of nine software-related metrics from software. Estimation of software reuse is done using the proposed C-RideNN algorithm. The C-RideNN algorithm estimates the software reuse factor.
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