An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

Gupta, Himanshu; Kulkarni, Tanmay Girish; Kumar, Lov; Neti, Lalita Bhanu Murthy; Krishna, Aneesh

doi:10.1007/978-3-030-75075-6_10

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…As shown in Figure 4 and Figure 5, SMOTE does achieve significant improvement over the None technique on Data Class, God Class, and Long Method across our data sets, and obtains non-significant improvement on Feature Envy. Therefore, researchers and practitioners may still consider using SMOTE as a preprocessing method in line with previous studies Akhter et al (2021); Alkharabsheh et al (2021); Gupta et al (2021); Jain and Saha (2021); Stefano et al (2021); Khleel and Nehéz (2022);Kovačević et al (2022); Nanda and Chhabra (2022); Yedida and Menzies (2022), but should also consider exploring other techniques that may be more effective. Our results in Section 5.3 demonstrate that SMOTE does not consistently achieve the best performance on all four data sets, and the top-performing data resampling technique outperforms SMOTE by 2.63%-17.73% in terms of MCC.…”

Section: Discussionmentioning

confidence: 67%

“…Table 1 lists the summary of the literature review, where the first column presents the first author and the publication year, the second column presents the types of code smells, the third column presents the used machine learning algorithms, and the fourth column presents the code smell features, and the last one presents the imbalanced learning methods used in the study. Some researchers Akhter et al (2021); Alkharabsheh et al (2021); Gupta et al (2021); Jain and Saha (2021); Stefano et al (2021); Khleel and Nehéz (2022); Kovačević et al (2022); Nanda and Chhabra (2022); Yedida and Menzies (2022) applied SMOTE as a data preprocessing method to alleviate the class imbalance problem and then utilized or proposed some more advanced algorithms for CSD. For example, Akhter et al Akhter et al (2021) used the four machine learning classifiers (i.e., NB, RF, DT, and SVM) to investigate the effect of machine learning techniques on CSD.…”

Section: Imbalanced Learning For Csdmentioning

confidence: 99%

“…Alkharabsheh et al Alkharabsheh et al (2021) extended two software metrics project domain and size category to detect code smells by using the eight machine learning classifiers (i.e., LibSVM, IBK, DT, JRip, SMO, NB, RF, and Random Committee (RC)). Gupta et al Gupta et al (2021) employed eight deep learning models for CSD. Jain et al Jain and Saha (2021) used hybrid feature selection and ensemble learning techniques to improve detection performance by adopting the 11 classifiers (i.e., SVM, KNN, NB, DT, Linear Discriminant Analysis (LDA), Logistic Regression (LR), Bagging, AdaBoost, XGBoost, Gradient Boost (GB), and Stacking).…”

Section: Imbalanced Learning For Csdmentioning

confidence: 99%

See 2 more Smart Citations

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: Discussionmentioning

confidence: 67%

Section: Imbalanced Learning For Csdmentioning

confidence: 99%

Section: Imbalanced Learning For Csdmentioning

confidence: 99%

See 1 more Smart Citation

On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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show abstract

“…Table 1 lists the summary of the literature review, where the first column presents the references of studies, the second column presents the types of code smells, the third column presents the used machine learning algorithms, and the fourth column presents the code smell features, and the last one presents the imbalanced learning methods used in the study. Some researchers [50][51][52][53][55][56][57][58][59][60][61] applied SMOTE as a data preprocessing technique to alleviate the class imbalance problems, and utilized or proposed some more advanced algorithms for CSD. For example, Akhter et al 50 used the four machine learning classifiers (i.e., NB, RF, DT, and SVM) to investigate the effect of machine learning techniques on CSD.…”

Section: Imbalanced Learning For 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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“…Gupta et al [39] recommended prediction of code smells using feature extraction from source code on eight types of code smells. They present the application of data sampling technique to handle the class imbalance problem and uses feature selection technique to find most relevant features sets.…”

Section: Related Workmentioning

confidence: 99%