A New Method for Software Defect Prediction Based on Optimized Machine Learning Techniques

Abstract

This thesis aimed to develop a robust neuro-computing model for software defect prediction, utilizing the Levenberg Marquardt Neural Network (LM-ANN) with a novel improved genetic algorithm as a heuristic model. The goal was to achieve higher accuracy and overcome local minima and convergence issues