Recommending Move Method Refactoring Opportunities Based on Feature Fusion and Deep Learning

The Move Method refactoring is crucial for mitigating the Feature Envy code smell, which enhances cohesion and reduces coupling by relocating methods to more suitable classes. Existing deep learning approaches often suffer from redundant features, limiting model generalization. To address this, this paper introduces GMove, a novel approach leveraging feature fusion and a hybrid deep learning architecture (Bi-LSTM and CNN branches) to recommend refactoring opportunities. By fusing semantic, structural, and metric features from a constructed 16,828-sample dataset, GMove effectively filters redundant information. Experimental results demonstrate that GMove achieves a high synthetic F1 score of 97.7% and significantly outperforms state-of-the-art refactoring tools, showing an average F1 improvement of 9.7% over the strongest modern baseline, affirming its effectiveness and novel fusion strategy.