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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.05835 |
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| _version_ | 1866908579326328832 |
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| author | Bhuiyan, Moinuddin Muhammad Imtiaz Hoque, Kazi Ekramul Islam, Rakibul Tusher, Md. Mahbubur Rahman Hassan, Najmul Tomioka, Yoichi Nishimura, Satoshi Shin, Jungpil Miah, Abu Saleh Musa |
| author_facet | Bhuiyan, Moinuddin Muhammad Imtiaz Hoque, Kazi Ekramul Islam, Rakibul Tusher, Md. Mahbubur Rahman Hassan, Najmul Tomioka, Yoichi Nishimura, Satoshi Shin, Jungpil Miah, Abu Saleh Musa |
| contents | This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, including XGBoost, AdaBoost, and other classifiers, alongside key techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and Pearson correlation for efficient feature selection. These methods collectively improve model accuracy and generalization. Our methodology involves several steps: first, we preprocess the data and apply SMOTE to balance the dataset; next, Pearson correlation is used for feature selection to reduce redundancy; followed by training eight ML algorithms and tuning hyperparameters through Grid Search, Random Search, and Bayesian Optimization. Finally, we evaluate the models using accuracy, F-measure, and confusion matrices. The results show that AdaBoost, Random Forest, and XGBoost perform best, achieving accuracies of 100%, 99%, and 99%, respectively. This study provides a robust framework for detecting code smells, enhancing software quality assurance, and demonstrating the effectiveness of a comprehensive, optimized ML approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization Bhuiyan, Moinuddin Muhammad Imtiaz Hoque, Kazi Ekramul Islam, Rakibul Tusher, Md. Mahbubur Rahman Hassan, Najmul Tomioka, Yoichi Nishimura, Satoshi Shin, Jungpil Miah, Abu Saleh Musa Computational Engineering, Finance, and Science This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, including XGBoost, AdaBoost, and other classifiers, alongside key techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and Pearson correlation for efficient feature selection. These methods collectively improve model accuracy and generalization. Our methodology involves several steps: first, we preprocess the data and apply SMOTE to balance the dataset; next, Pearson correlation is used for feature selection to reduce redundancy; followed by training eight ML algorithms and tuning hyperparameters through Grid Search, Random Search, and Bayesian Optimization. Finally, we evaluate the models using accuracy, F-measure, and confusion matrices. The results show that AdaBoost, Random Forest, and XGBoost perform best, achieving accuracies of 100%, 99%, and 99%, respectively. This study provides a robust framework for detecting code smells, enhancing software quality assurance, and demonstrating the effectiveness of a comprehensive, optimized ML approach. |
| title | Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.05835 |