A smell in software source code denotes an indication of suboptimal design and implementation decisions, potentially hindering the code understanding and, in turn, raising the likelihood of being prone to changes and faults. Identifying these code issues at an early stage in the software development process can mitigate these problems and enhance the overall quality of the software. Current research primarily focuses on the utilization of deep learning-based models to investigate the contextual information concealed within source code instructions to detect code smells, with limited attention given to the importance of structural and design-related features. This paper proposes a novel approach to code smell detection, constructing a deep learning architecture that places importance on the fusion of structural features and statistical semantics derived from pre-trained models for programming languages. We further provide a thorough analysis of how different source code embedding models affect the detection performance with respect to different code smell types. Using four widely-used code smells from well-designed datasets, our empirical study shows that incorporating design-related features significantly improves detection accuracy, outperforming state-of-the-art methods on the MLCQ dataset with improvements ranging from 5.98% to 28.26%, depending on the type of code smell.

Code smells is the term used to signal certain patterns or structures in software code that may contain a potential design or architecture problem, leading to maintainability or other software quality issues. Detecting code smells early in the software development process helps prevent these problems and improve the overall software quality. Existing research concentrates on the process of collecting and handling dataset, then exploring the potential of utilizing deep learning models to detect smells, while ignoring extensive feature engineering. Though these approaches obtained promising results, the following issues need to be tackled: (i) extracting both structural and semantic features from the software units; (ii) mitigating the effects of imbalanced data distribution on the performance.In this paper, we propose DeepSmells as a novel approach to code smells detection. To learn the complex hierarchical representations of the code fragment, we apply a deep convolutional neural network (CNN). Then, in order to improve the quality of the context encoding and preserve semantic information, long short-term memory networks (LSTM) is placed immediately after the CNN. The final classification is conducted by deep neural networks with weighted loss function to reduce the impact of skewed data distribution. We performed an empirical study using the existing code smell benchmark datasets to assess the performance of our proposed approach, and compare it with state-of-the-art baselines. The results demonstrate the effectiveness of our proposed method for all kinds of code smells with outperformed evaluation metrics in terms of F1 score and MCC.