Feature Weight-Based Optimization in Software Development Model Using Meta Heuristic Machine-Learning Algorithms

 

 

 

N. Durga Devi1, Tirimula Rao Benala2

 

1Research Scholar, Department of Computer Science and Engineering-Information Technology, Jawaharlal Nehru Technological University Gurajada Vizianagaram, Dwarapudi, Vizianagaram, Andhra Pradesh-535003, India

 

2Department of Information Technology, JNTU-GV College of Engineering, Vizianagaram, Jawaharlal Nehru Technological University Gurajada Vizianagaram, Dwarapudi, Vizianagaram, Andhra Pradesh-535003, India

 

Text Box: Abstract System users are increasingly interested in software correctness and efficiency checks prior to usage. Programmers in the twenty-first century are therefore making a conscious effort to create software that is more accurate, more efficient, and less prone to bugs. A software development model utilizing metaheuristic machine learning algorithms involves using metaheuristic optimization techniques to enhance various aspects of the software development lifecycle, such as optimizing machine learning models, hyperparameters, and even software architecture. This research propose novel technique in feature weight model based optimization in software development utilizing Meta heuristic ML method. Here the feature weight and feature selection is carried out for software model using support additive regression Laplacian score perceptron neural network. Then the software model parameter optimization is carried out using ant binary swarm component encoder optimization method. Simulation analysis is carried out in terms of training accuracy, MAR (Mean absolute residual), Mean balanced relative error (MBRE), F-measure. Emails: durgadevisd3@gmail.com; b.tirimula@gmail.com

 

Received: March 05, 2025 Revised: June 03, 2025 Accepted: July 08, 2025

 

Keywords: Feature weight model; Software development; Meta heuristic; Machine learning model; Regression Laplacian score