MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang, Yang; Dong, Chunhao

doi:10.1002/smr.2403

Cited by 17 publications

(8 citation statements)

References 41 publications

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“…Their results presented that the accuracy and validity of these two methods for detecting code smell still need further investigation. Some studies Sharma et al (2021); Yu et al (2021); Zhang and Dong (2021); Li and Zhang;Zhang et al (2022) tried to apply deep learning techniques for CSD, and their conclusions examined that some deep learning models accept a better performance on CSD, i.e., Convolutional Neural Networks (CNN-1) Sharma et al (2021); Zhang et al (2022), recurrent neural network Sharma et al (2021), long short-term memory Yu et al (2021); Li and Zhang, residual network Zhang and Dong (2021), and attention Zhang et al (2022).…”

Section: Literature Reviewmentioning

confidence: 99%

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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Machine learning-based code smell detection has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for code smell detection, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques on machine learning-based code smell detection, we examine 31 imbalanced learning techniques with seven classifiers to build code smell detection models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed-rank test and Cliff’s δ. The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over-sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best-performing imbalanced learning techniques and the top-3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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Section: Literature Reviewmentioning

confidence: 99%

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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show abstract

“…Their results presented that the accuracy and validity of these two methods for detecting code smell still need further investigation. Some studies [44][45][46][47][48] tried to apply deep learning techniques for CSD, and their conclusions examined that some deep learning models accept a better performance on CSD, that is, convolutional neural networks (CNN-1), 44,48 recurrent neural network, 44 long short-term memory, 45,47 residual network, 46 and attention. 48…”

Section: Machine Learning-based Csdmentioning

confidence: 99%

On the relative value of imbalanced learning for code smell detection

Zou

Keung

et al. 2023

Softw Pract Exp

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SummaryMachine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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“…Zhang et al 28 introduced MARS, a brain-inspired method for code smell detection that relies on the Metric-Attention method. They applied various ML and Deep learning models and found that MARS outperformed conventional techniques in terms of accuracy.…”

Section: Background/literature Reviewmentioning

confidence: 99%

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

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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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MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Cited by 17 publications

References 41 publications

On the Relative Value of Imbalanced Learning for Code Smell Detection

On the Relative Value of Imbalanced Learning for Code Smell Detection

On the relative value of imbalanced learning for code smell detection

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

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