Predicting Software Quality: A Study of Machine Learning Methods

This study investigates machine learning techniques for software quality prediction. To evaluate software metrics and spot possible flaws, the suggested system makes use of techniques like Random Forest, Support Vector Machines (SVM), and Neural Networks. Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) are two feature selection methods that are used to increase model accuracy. The model's high precision and recall in predicting software quality are tested using datasets that are made available to the public. By enabling early problem discovery and enhancing program reliability, this method offers software developers and quality assurance teams an efficient tool.