## 6 Conclusions  
This paper proposes a novel approach, MovePerf, to predict the performance impact of MMR. Firstly, we manually applied MMR to 15 Java projects on GitHub, ensuring that only the move method operation was performed. By analyzing the projects before and after refactoring, 23 class-level, 8 method-level, and 1 semantic-level features that were most relevant to the refactoring operation were determined. Secondly, JMH was used to measure the execution time with high precision, ensuring the accuracy of the performance labels used in the dataset. Finally, a prediction model consisting of FM and a DFNN was constructed, where FM was responsible for capturing low-order interactions between the features, while the DFNN modeled high-order interactions to provide more accurate predictions. MovePerf was evaluated in four real-world large-scale projects. The experimental results showed that MovePerf achieved higher prediction accuracy, with an average MRE of 7.69%, outperforming the CNN, FM, DeepFM, DeepPerf, and HINNPerf by 1.83% to 8.61%. Additionally, the model exhibited greater stability in most projects. Our proposed approach provides developers with a predictive framework to assess the performance impacts of specific refactoring operations prior to implementation, thereby mitigating the potential performance degradation in production environments. The operational workflow comprises three key phases: (1) the feature extraction of target classes and methods post-refactoring, (2) model inference to predict the execution time, and (3) data-driven refactoring decisions based on the prediction outcomes. It should be noted that, while pre-trained models are available for established project datasets, new projects require dataset construction and model fine-tuning prior to deploying MovePerf. Future research directions include exploring the locations of classes that can be moved to by the move method, expanding the dataset's size to enhance the model's robustness and generalizability, and continuing to select suitable projects to further enrich the diversity and quantity of datasets used in building the model, which would allow for the broader application and refinement of the MovePerf framework.  
Y.Y.: experimental analysis, writing--original draft,review and editing; Y.L.: experimentation, writing--original draft, review and editing; S.L.: experimentation, writing--original draft, review and editing; X.Z.: writing--review and editing; L.Z.: writing--review and editing; Y.B.: proposal of idea, writing--review and editing; Y.Z.: proposal of idea, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Natural Science Foundation of Hebei Province under Grant No. F2023208001 and the Overseas High-Level Talent Foundation of Hebei Province under Grant No. C20230358.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors gratefully acknowledge the insightful comments and suggestions of the reviewers, which improved the presentation of this work.  
The authors declare no conflicts of interest.