Alleviating class imbalance in Feature Envy prediction: An oversampling technique based on code entity attributes

Context:Feature Envy is a common code smell that occurs when a method heavily relies on data or functionality from other classes. Detecting Feature Envy is essential for improving software modularity and reducing technical debt. However, real-world datasets often exhibit severe class imbalance, with far fewer Feature Envy instances than non-smelly ones, posing challenges for prediction models. Traditional oversampling techniques attempt to address this issue by relying solely on numerical vectors but often fail to capture the complex relationships between code entities, potentially deviating from the nature of Feature Envy.