Abstract
Code smell detection is critical for calculating system quality and identifying issues that require more work and development. The technique of finding wrongly developed code components and implementing them is known as code smell detection. In this study, we used two method-level code smell datasets: the long parameter list and the switch statement, for detecting the code smells. A SMOTE class balancing approach is utilized to deal with the issue of class imbalance in datasets. A wrapper-based feature selection approach is used to choose the best features from each dataset. We applied three ensemble learning-based machine learning methods. To validate the model's accuracy, we utilized a fivefold cross-validation technique with five performance measurements (precision, recall, F-measure, AUC_Score, and accuracy). Using the max voting dataset, we obtained the best accuracy of 97.12% for the long parameter list dataset.
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Dewangan, S., Rao, R.S. (2023). Method-Level Code Smells Detection Using Machine Learning Models. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_7
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DOI: https://doi.org/10.1007/978-981-99-3734-9_7
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