Code Smell Detection Using Ensemble Machine Learning Algorithms

Code smells are the result of not following software engineering principles during development, especially in design and coding phase. It leads to low maintainability. To evaluate quality its maintainability, code smell detection can be helpful. Many machine learning algorithms being used detect smells. In this study, we applied five ensemble two deep Four datasets were analyzed: Data class, God Feature-envy, Long-method datasets. previous works, stacking dataset results found acceptable, but there is scope improvement. A class balancing technique (SMOTE) was handle imbalance problem The Chi-square feature extraction select more relevant features each dataset. All obtained highest accuracy—100% for with different selected sets metrics, poorest accuracy, 91.45%, achieved by Max voting method Feature-envy twelve metrics.