Deep semantic-based feature envy identification

Code smells regularly cause potential software quality problems in software development. Thus, code smell detection has attracted the attention of many researchers. A number of approaches have been suggested in order to improve the accuracy of code smell detection. Most of these approaches rely solely on structural information (code metrics) extracted from source code and heuristic rules designed by people. In this paper, We propose a method-representation based model to represent the methods in textual code, which can effectively reflect the semantic relationships embedded in textual code. We also propose a deep learning based approach that combines method-representation and a CNN model to detect feature envy. The proposed approach can automatically extract semantic and features from textual code and code metrics, and can also automatically build complex mapping between these features and predictions. Evaluation results on open-source projects demonstrate that our proposed approach achieves better performance than the state-of-the-art in detecting feature envy.