Predicting Execution Time for Move Method Refactoring Base on Deep Learning

Move method refactoring (MMR) is one of the most commonly used software maintenance techniques to improve feature envy. Existing work focuses on how to identify and recommend MMR. However, little is known about how MMR impacts program performance. There is a gap between MMR and its impact on performance. To reduce this gap, this paper proposes a novel approach to predict MMR performance based on deep learning and feature interaction. Firstly, construction of the first dataset in this domain. Secondly, FMPerf is built by combining Factor Machine and DeepPerf. Finally, We evaluate FMPerf on real-world projects including JUnit, LC-problems, and Kevin. The experimental results show that FMPerf obtains an average of 7.31 % MRE, illustrating that the predicted value is close to the real value. Furthermore, FMPerf improves MRE ranging from 2.29 % to 11.02 % compared to existing approaches, including Convolutional Neural Networks(CNN), Factorization-Machine based Neural Network(DeepFM), Performance Prediction with Deep Sparse Neural Network(DeepPerf), and Hierarchical Inter-action Neural Network for Performance Prediction(HINNPerf), demonstrating its effectiveness.