code-smell-detection-survey

Code Smell Detection with Machine Learning Papers

Relevant Papers

  1. S. Alawadi, K. Alkharabsheh, F. Alkhabbas, V. R. Kebande, F. M. Awaysheh, F. Palomba, and M. Awad, “FedCSD: A Federated Learning Based Approach for Code-Smell Detection,” IEEE Access, vol. 12, pp. 44888–44904, 2024, doi: 10.1109/ACCESS.2024.3380167.

  2. M. Siksna, I. Berzina, and A. Romanovs, “Machine Learning Powered Code Smell Detection as a Business Improvement Tool,” in 2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2023, pp. 1–6, doi: 10.1109/ITMS59786.2023.10317724.

  3. H. Nanadani, M. Saad, and T. Sharma, “Calibrating Deep Learning-based Code Smell Detection using Human Feedback,” in 2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023, pp. 37–48, doi: 10.1109/SCAM59687.2023.00015.

  4. A. Nizam, M. Y. Avar, Ö. K. Adaş, and A. Yanık, “Detecting Code Smell with a Deep Learning System,” in 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), 2023, pp. 1–5, doi: 10.1109/ASYU58738.2023.10296577.

  5. H. M. Yahya and D. B. Taha, “Detection Bad Code Smells By Using Deep Machine Learning Approaches,” in 2023 1st International Conference on Advanced Engineering and Technologies (ICONNIC), 2023, pp. 281–286, doi: 10.1109/ICONNIC59854.2023.10467672.

  6. D. Mahalakshmi, P. Kasinathan, D. Elangovan, C. R. Bhat, M. Balamurugan, and S. Sivakumar, “Code Smell Detection using Hybrid Machine Learning Algorithms,” in 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), 2023, pp. 633–638, doi: 10.1109/ICIRCA57980.2023.10220911.

  7. N. Vatanapakorn, C. Soomlek, and P. Seresangtakul, “Python code smell detection using machine learning,” in 2022 26th International Computer Science and Engineering Conference (ICSEC), IEEE, 2022.

  8. S. Dewangan, R. S. Rao, and P. S. Yadav, “Dimensionally Reduction based Machine Learning Approaches for Code Smells Detection,” in 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 2022, pp. 1–4, doi: 10.1109/ICICCSP53532.2022.9862030.

  9. M. Zhang et al., “MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network,” Wiley Journal, 2021.

  10. S. Jain et al., “Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection,” Engineering Village Journal, 2021.

  11. H. Aljamaan, “Voting Heterogeneous Ensemble for Code Smell Detection,” in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021, pp. 897–902, doi: 10.1109/ICMLA52953.2021.00148.

  12. J. Oliveira et al., “Applying Machine Learning to Customized Smell Detection: A Multi-Project Study,” in Engineering Village Conference, 2020.

  13. H. Mhawish et al., “Predicting Code Smells and Analysis of Predictions: Using Machine Learning Techniques and Software Metrics,” Engineering Village Journal, 2020.

  14. M. Barbez et al., “A machine-learning based ensemble method for anti-patterns detection,” Science Direct Journal, 2020.

  15. M. Pecorelli et al., “A large empirical assessment of the role of data balancing in machine-learning-based code smell detection,” Science Direct Journal, 2020.

  16. G. Cruz et al., “Detecting bad smells with machine learning algorithms: an empirical study,” in ACM Conference, 2020.

17.J. A. Jesudoss et al., “Identification of Code Smell Using Machine Learning,” in IEEE Xplore Conference, 2019.

  1. M. Pecorelli et al., “Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection,” in IEEE Xplore Conference, 2019.

  2. I. Kiyak et al., “Comparison of Multi-Label Classification Algorithms for Code Smell Detection,” in IEEE Xplore Symposium, 2019.

  3. R. Pritam et al., “Assessment of Code Smell for Predicting Class Change Proneness Using Machine Learning,” IEEE Xplore Journal, 2019.

  4. K. Lafi et al., “Code Smells Analysis Mechanisms, Detection Issues, and Effect on Software Maintainability,” in IEEE Xplore Conference, 2019.

  5. R. Gupta et al., “An Empirical Framework for Code Smell Prediction using Extreme Learning Machine,” in IEEE Xplore Conference, 2019.

  6. M. Pecorelli et al., “On the role of data balancing for machine learning-based code smell detection,” in ACM Workshop, 2019.

  7. X. Guo et al., “Deep Semantic-Based Feature Envy Identification,” in ACM Symposium, 2019.

  8. M. Rubin et al., “Sniffing Android Code Smells: An Association Rules Mining-Based Approach,” in ACM Conference, 2019.

  9. F. Luiz et al., “Machine Learning Techniques for Code Smells Detection: An Empirical Experiment on a Highly Imbalanced Setup,” in ACM Conference, 2019.

  10. M. Di Nucci et al., “Detecting Code Smells Using Machine Learning Techniques: Are We There Yet?,” in IEEE Xplore Conference, 2018.

  11. A. Hadziabdic et al., “Comparison of Machine Learning Methods for Code Smell Detection Using Reduced Features,” in IEEE Xplore Conference, 2018.

  12. H. Hozano et al., “Evaluating the Accuracy of Machine Learning Algorithms on Detecting Code Smells for Different Developers,” in Engineering Village Conference, 2017.

  13. F. A. Fontana et al., “Code Smell Severity Classification Using Machine Learning Techniques,” Science Direct Journal, 2017.

  14. S. Kaur et al., “A Support Vector Machine Based Approach for Code Smell Detection,” in IEEE Xplore Conference, 2017.

  15. H. Hozano et al., “Smells Are Sensitive to Developers! On the Efficiency of (Un)Guided Customized Detection,” in ACM Conference, 2017.

  16. H. Hozano et al., “Using Developers’ Feedback to Improve Code Smell Detection,” in Engineering Village Symposium, 2016.

  17. L. Amorim, E. Costa, N. Antunes, B. Fonseca, and M. Ribeiro, “Experience Report: Evaluating the Effectiveness of Decision Trees for Detecting Code Smells,” in 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE), IEEE, 2015, pp. 261–269.

  18. F. A. Fontana et al., “Comparing and Experimenting Machine Learning Techniques for Code Smell Detection,” Springer Link Journal, 2015.

  19. C. F. R. Conceicao, G. de Figueiredo Carneiro, and F. B. e. Abreu, “Streamlining Code Smells: Using Collective Intelligence and Visualization,” in 2014 9th International Conference on the Quality of Information and Communications Technology, IEEE, 2014, pp. 306–311.

  20. F. A. Fontana et al., “Code Smell Detection: Towards a Machine Learning-Based Approach,” in 2013 IEEE International Conference on Software Maintenance, IEEE, 2013, pp. 396–399.

  21. F. Khomh et al., “A Bayesian Approach for the Detection of Code and Design Smells,” in 2009 Ninth International Conference on Quality Software, IEEE, 2009, pp. 305–314.