DIR-SMOTE: a density-influence resampling framework for imbalanced code smell detection

Abstract

Code smell detection is vital for ensuring software quality, but the imbalance between smelly and non-smelly code instances impairs detection, especially for minority smells like Data Class and Feature Envy. Existing oversampling techniques, such as Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE (BL-SMOTE), and Adaptive Synthetic (ADASYN), attempt to mitigate this issue but often introduce noise or semantically irrelevant samples. This study proposes DIR-SMOTE (Density and Influence-based Resampling using SMOTE), a density and explanation-guided resampling framework that integrates local density estimation and SHapley Additive exPlanations (SHAP)-based feature importance to improve the quality of synthetic minority samples. Initially, DIR-SMOTE filters out noisy or isolated minority instances using density metrics. It then employs SHAP to identify the most influential features per instance. Synthetic samples are generated by interpolating between dense neighbors while perturbing only top-ranked SHAP features, thereby preserving semantic integrity. DIR-SMOTE is evaluated on five benchmark datasets, namely, Apache, jEdit, EDTForCSD, DesigniteJava, and MLCQ, across multiple smells such as Long Method, Feature Envy, and Data Class. Compared to nine standard resampling methods, DIR-SMOTE achieves up to 6.7% improvement in F1-score and 5.1% in precision, consistently enhancing smelly code detection in both binary and multiclass settings. Rather than relying on complex generative models, DIR-SMOTE focuses on explanation-guided and density-aware sample generation that remains transparent and computationally efficient. Overall, it offers a lightweight and robust solution that can be seamlessly integrated into practical quality assurance workflows, including automated smell detection tools and IDE-based analyzers.