Revealing MOVE METHOD: Machine Learning-driven Real-time Refactoring Identification for Enhanced Software Quality

Background: Refactoring is the process of restructuring code without changing its external behaviour. This study examines different outlooks on refactoring from multiple sources, underscoring its significance in ensuring software quality and manageability. It focuses on how developers change their code without changing its external appearance. Objective: In this study, we present with a methodology for identifying the refactoring opportunities in a source code through the application of machine learning techniques. These methods enable us to identify refactoring opportunities in real time, customized to fit the specific features of the projects and developers involved. Our approach gathers information about previous changes made to improve the features. The objective of this work is to enhance and optimize computer code, commonly referred to as refactoring, with the ultimate aim of producing high-quality software.