Pre-trained Model Based Feature Envy Detection

Code smells slow down software system development and makes them harder to maintain. Existing research aims to develop automatic detection algorithms to reduce the labor and time costs within the detection process. Deep learning techniques have recently been demonstrated to enhance the performance of recognizing code smells even more than metric-based heuristic detection algorithms. As large-scale pre-trained models for Programming Languages (PL), such as CodeT5, have lately achieved the top results in a variety of downstream tasks, some researchers begin to explore the use of pre-trained models to extract the contextual semantics of code to detect code smells. However, little research has employed contextual code semantics relationship between code snippets obtained by pre-trained models to identify code smells. In this paper, we investigate the use of the pre-trained model CodeT5 to extract semantic relationships between code snippets to detect feature envy, which is one of the most common code smells. In addition, to investigate the performance of these semantic relationships extracted by pre-trained models of different architectures on detecting feature envy, we compare CodeT5 with two other pre-trained models CodeBERT and CodeGPT. We have performed our experimental evaluation on ten open-source projects, our approach improves F-measure by 29.32% on feature envy detection and 16.57% on moving destination recommendation. Using semantic relations extracted by several pre-trained models to detect feature envy outperforms the state-of-the-art. This shows that using this semantic relation to detect feature envy is promising. To enable future research on feature envy detection, we have made all the code and datasets utilized in this article open source.