Severity Classification of Code Smells Using Machine-Learning Methods

Dewangan, Seema; Rao, Rajwant Singh; Chowdhuri, Sripriya Roy; Gupta, Manjari

doi:10.1007/s42979-023-01979-8

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…They applied different methodologies, which are shown in Table 10 . Table 10 compares our outcomes with Fontana et al 6 , Abdou et al 12 , and Dewangan et al 25 . Fontana et al 6 applied eighteen ML models and implemented binary classification, multinomial classification, and regression technique with linear co-relation filter method.…”

Section: Discussion and Results Analysismentioning

confidence: 80%

“…This section constructs a comparative summary of proposed approach's result with other relevant research studies. To the best of our knowledge and available literature on CSS detection, only three authors (Fontana et al 6 ; Abdou et al 12 ; Dewangan et al 25 ) have studied the severity dataset. They applied different methodologies, which are shown in Table 10 .…”

Section: Discussion and Results Analysismentioning

confidence: 99%

“…Abdou et al 12 applied forty binary and multinomial classification techniques with a ranking correlation algorithm. Dewangan et al 25 applied seven ML and ensemble methods. Our approach applied five ML models (LR, MLP, RF, DT, and KNN) with PCA-based Feature selection and SMOTE class balancing techniques.…”

Section: Discussion and Results Analysismentioning

confidence: 99%

“…The comparison for each dataset is shown in the following points: For the DC dataset, in our approach, DT model detected the highest Severity Accuracy Score of 0.83, while the Fontana et al 6 detected a Severity Accuracy Score of 0.77 applying the O-RF method and Abdou et al 12 detected a Severity Accuracy Score of 0.93 applying the O-R-SMO method. Dewangan et al 25 detected a Severity Accuracy Score of 0.88 using gradient boosting model. Therefore, the Abdou et al 12 approach is good.…”

Section: Discussion and Results Analysismentioning

confidence: 99%

“…Dewangan et al 25 utilized four ML (LR, RF, KNN, DT) and three ensemble models (AdaBoost, XG Boost, and Gradient Boosting) to detect CSS from four datasets. They used chi-square FST and two-parameter optimization methods (Grid search and Random search).…”

Section: Background/literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Rao,

Dewangan,

Mishra

et al. 2023

Sci Rep

Self Cite

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Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system's maintainability. It is quite essential to assess the severity of the code smells detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances the difficulties in code smell severity detection. In this study, four code smell severity datasets (Data class, God class, Feature envy, and Long method) are selected to detect code smell severity. In this work, an effort is made to address the issue of class imbalance, for which, the Synthetic Minority Oversampling Technique (SMOTE) class balancing technique is applied. Each dataset's relevant features are chosen using a feature selection technique based on principal component analysis. The severity of code smells is determined using five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, and Logistic Regression. This study obtained the 0.99 severity accuracy score with the Random forest and Decision tree approach with the Long method code smell. The model's performance is compared based on its accuracy and three other performance measurements (Precision, Recall, and F-measure) to estimate severity classification models. The impact of performance is also compared and presented with and without applying SMOTE. The results obtained in the study are promising and can be beneficial for paving the way for further studies in this area.

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Section: Discussion and Results Analysismentioning

confidence: 80%

Section: Discussion and Results Analysismentioning

confidence: 99%

Section: Discussion and Results Analysismentioning

confidence: 99%

Section: Discussion and Results Analysismentioning

confidence: 99%

Section: Background/literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Rao,

Dewangan,

Mishra

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Multi-label learning for identifying co-occurring class code smells

Hadj-Kacem,

Bouassida

2024

Computing

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Exploring the role of project status information in effective code smell detection

Alkharabsheh,

Alawadi,

Crespo

et al. 2024

Cluster Comput

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Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection.

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Severity Classification of Code Smells Using Machine-Learning Methods

Cited by 10 publications

References 29 publications

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Multi-label learning for identifying co-occurring class code smells

Exploring the role of project status information in effective code smell detection

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