## 1 Introduction  
Refactoring is a software evolution technique used to improve a program's internal structure without altering its external behavior. It has been widely used to enhance source code's readability, maintainability, and extensibility [1, 2]. Move method refactoring (MMR) is one of the most commonly used operations to improve feature envy by transferring a method from one class to a more suitable class [3]. MMR aims to enhance the source code's structure and optimize class responsibilities, addressing issues including unclear code organization, low code reuse, and high coupling between classes. Many works have been conducted on MMR [3, 4, 5, 6]. Some works focus on MMR recommendations. JMove [4] provides refactoring recommendations by comparing the similarity of the dependencies established by a method with the dependencies established by the methods in possible target classes. QMove [5] relies on the measurements of the Quality Model for Object Oriented Design (QMOOD) to recommend MMR that improves the software quality. RMove [6] implements a method that automatically learns and concatenates both structural and semantic representations of code fragments, respectively,and further trains machine learning classifiers to guide the movement of the method to suitable classes. Some works evaluate whether MMR can enhance software quality attributes in real-world applications [7; 8]. For instance, [PERSON] et al. [7] argued that refactoring does not necessarily improve software's external quality attributes. [PERSON] et al. [8] studied the influences of various refactoring operations, including the move method, on system quality. Furthermore, some researchers investigate the impact of MMR on software performance [9; 10]. For example, [PERSON] et al. [9] found that not all refactoring operations effectively improve the program execution performance based on comparisons of the execution times of four benchmark programs. [PERSON] et al. [10] explored this by comparing the execution times of code before and after refactoring. Although many studies have been conducted on MMR, there is a gap is knowledge regarding MMR and its performance impact. Existing works demonstrate that the impact of refactoring on performance can be bidirectional, which means that it may either improve or degrade the performance depending on the context [8; 10]. MMR's performance impact varies across different projects. The current research has not yet explored refactoring's influence on performance. As a result, researchers face challenges in accurately predicting whether a particular refactoring operation will lead to a substantial performance gain or loss. While deep learning models for performance prediction have demonstrated strong capabilities, these technologies have not been widely applied to code refactoring. Specifically, performance prediction models for refactoring are still underexplored and represent a critical gap in the field. Bridging this gap, one could explore the specific degree and scale of the performance impact of refactoring. Two key issues need to be considered. Firstly, there is a scarcity of suitable datasets for this research. An ideal dataset would include many different MMR results and reliable execution times. Secondly, it is necessary to construct a prediction model tailored to the dataset's characteristics. A well-designed model would enable accurate performance prediction for MMR. To address these challenges, this paper proposes a novel approach named MovePerf to predict the performance impact of MMR. On the one hand, this method identifies 32 features, including 31 metric-based and 1 semantic-based features, based on observations of actual projects. After this, it uses JMH to measure the execution times of the project under specific configurations, with these times serving as the labels to build the dataset. On the other hand, we build a model composed of a factor machine (FM) and a deep feedforward neural network (DFNN) to predict performance.