Hybrid optimization driven ridenn for software reusability estimation

Abstract EN

Measuring the software reusability has become a prime concern in maintaining the quality of the software.

Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges.

This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time.

Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor.

Initially, nine software related metrics are extracted from the software.

Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software.

Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges.

This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time.

Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor.

Initially, nine software related metrics are extracted from the software.

Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation.

The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose.

Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM).

The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM.