Seema Dewangan

Code smells, which represent poor design choices or suboptimal code implementations, reduce software quality and hinder the code maintenance process. Detecting code smells is, therefore, essential during software development. This study introduces a Python-based code smell dataset targeting two smell types: Large Class and Long Method. Five ensemble learning methods—Bagging, Gradient Boost, Max Voting, AdaBoost, and XGBoost—were employed to detect code smells within these datasets. The ten most significant features were selected using the Chi-square feature selection technique. To address the class imbalance, the SMOTE algorithm was applied. Experimental results yielded a best accuracy score of 0.96 and an MCC of 0.85 for the Large Class dataset using the Max Voting model. For the Long Method dataset, a best accuracy score of 0.98 and an MCC of 0.94 were achieved using the Gradient Boost model in conjunction with Chi-square feature selection. These results highlight the effectiveness of the proposed methodology and its potential to enhance code smell detection in Python significantly, reinforcing confidence in the approach’s thoroughness and applicability.

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