Enhancing Refactoring Prediction at the Method-Level Using Machine Learning Approach

Abstract

Software refactoring is a common activity in software engineering that aims to improve the structural quality of the source code without changing its external behavior or affecting its functionality. Traditional refactoring methods rely heavily on developer expertise, making the process time-consuming and error-prone. Recent advances in machine learning (ML) have shown promising results in identifying optimal refactoring opportunities by analyzing and detecting hidden patterns in software data. However, machine learning techniques typically require large datasets for effective training. To address this problem, meta-learning has emerged as a promising approach for identifying refactoring opportunities with limited data, relying on past experiences from similar tasks. This paper proposes an innovative approach that combines stacking and boosting classifiers with feature selection algorithms to enhance refactoring prediction accuracy. The proposed approach is evaluated using open source Java projects, and the results demonstrate significant improvements in prediction efficiency compared to traditional machine learning methods.