Unearthing Code Smells: An In‐Depth Exploration of Machine Learning Techniques in Code Smell Detection.
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| Title: | Unearthing Code Smells: An In‐Depth Exploration of Machine Learning Techniques in Code Smell Detection. |
|---|---|
| Authors: | Kheria, Ishita1 (AUTHOR), Gada, Dhruv1 (AUTHOR), Harkare, Vijay1 (AUTHOR), Karani, Ruhina1 (AUTHOR) ruhina.karani@djsce.ac.in |
| Source: | Journal of Software: Evolution & Process. Jun2026, Vol. 38 Issue 6, p1-19. 19p. |
| Subjects: | Machine learning, Design failures, Classification algorithms, Feature extraction, Detection algorithms, Model validation, Maintainability (Engineering) |
| Abstract: | Code smells are clear indicators of design flaws that compromise maintainability, reliability, and overall performance in software systems. As systems become complex, traditional detection methods become inadequate, prompting the need for automated approaches. In this study, we evaluate the potential of conventional machine learning algorithms for detecting critical code smells. Specifically, we examine Classification and Regression Trees (CART), Gradient Boosting Classifiers, Extreme Gradient Boosting (XGBoost), RuleFit, Adaptive Boosting (AdaBoost), K‐Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), and Random Forest to identify six key code smell categories: Large Class, Long Parameter List, Switch Statements, God Class, Data Class, and Long Method. Advanced resampling techniques such as SMOTE–ENN and SMOTE–Tomek are employed to mitigate dataset imbalances alongside tree‐based feature extraction for effective dimensionality reduction. Systematic experimentation demonstrates that SMOTE‐based strategies significantly enhance detection performance, yielding robust results across multiple evaluation metrics. By focusing on the core challenges of code smell detection, this study provides a cost‐effective alternative to deep learning methods, which often demand extensive computational resources and large labeled datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194811399 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unearthing Code Smells: An In‐Depth Exploration of Machine Learning Techniques in Code Smell Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kheria%2C+Ishita%22">Kheria, Ishita</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gada%2C+Dhruv%22">Gada, Dhruv</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harkare%2C+Vijay%22">Harkare, Vijay</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Karani%2C+Ruhina%22">Karani, Ruhina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ruhina.karani@djsce.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Software%3A+Evolution+%26+Process%22">Journal of Software: Evolution & Process</searchLink>. Jun2026, Vol. 38 Issue 6, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Design+failures%22">Design failures</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Maintainability+%28Engineering%29%22">Maintainability (Engineering)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Code smells are clear indicators of design flaws that compromise maintainability, reliability, and overall performance in software systems. As systems become complex, traditional detection methods become inadequate, prompting the need for automated approaches. In this study, we evaluate the potential of conventional machine learning algorithms for detecting critical code smells. Specifically, we examine Classification and Regression Trees (CART), Gradient Boosting Classifiers, Extreme Gradient Boosting (XGBoost), RuleFit, Adaptive Boosting (AdaBoost), K‐Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), and Random Forest to identify six key code smell categories: Large Class, Long Parameter List, Switch Statements, God Class, Data Class, and Long Method. Advanced resampling techniques such as SMOTE–ENN and SMOTE–Tomek are employed to mitigate dataset imbalances alongside tree‐based feature extraction for effective dimensionality reduction. Systematic experimentation demonstrates that SMOTE‐based strategies significantly enhance detection performance, yielding robust results across multiple evaluation metrics. By focusing on the core challenges of code smell detection, this study provides a cost‐effective alternative to deep learning methods, which often demand extensive computational resources and large labeled datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/smr.70144 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Design failures Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Detection algorithms Type: general – SubjectFull: Model validation Type: general – SubjectFull: Maintainability (Engineering) Type: general Titles: – TitleFull: Unearthing Code Smells: An In‐Depth Exploration of Machine Learning Techniques in Code Smell Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kheria, Ishita – PersonEntity: Name: NameFull: Gada, Dhruv – PersonEntity: Name: NameFull: Harkare, Vijay – PersonEntity: Name: NameFull: Karani, Ruhina IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20477473 Numbering: – Type: volume Value: 38 – Type: issue Value: 6 Titles: – TitleFull: Journal of Software: Evolution & Process Type: main |
| ResultId | 1 |
