Code smell detection using feature selection and stacking ensemble: An empirical investigation

Alazba, Amal; Aljamaan, Hamoud

doi:10.1016/j.infsof.2021.106648

Cited by 44 publications

(55 citation statements)

References 41 publications

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“…Their results showed that SMOTE could not improve the God Class detection performance. Alazba et al Alazba and Aljamaan (2021) studied the effect of the stacking ensemble on six code smells, and their conclusions examined that the performance of Stacking with LR and SVM is better than all individual classifiers. Aljamaan investigated the performance of the voting ensemble on CSD.…”

Section: Imbalanced Learning For Csdmentioning

confidence: 99%

“…These rules may be manually generated by domain experts Fontana et al (2016). However, both of these approaches can be time-consuming and cognitively demanding for software engineers Alazba and Aljamaan (2021), leading to a shift towards the use of machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Alkharabsheh et al Alkharabsheh et al (2022) and Pecorelli et al Pecorelli et al (2019a found that Synthetic Minority Over-sampling TEchnique (SMOTE) could not significantly improve detection performance. Alazba et al Alazba and Aljamaan (2021) and showed that the stacking ensemble and voting ensemble are better than individual classifiers.…”

Section: Introductionmentioning

confidence: 99%

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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: Imbalanced Learning For Csdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Relative Value of Imbalanced Learning for Code Smell Detection

Xiao

Zou

et al. 2023

Preprint

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“…FS is the process of identifying and removing the irrelevant and redundant features to improve the performance of the classifier. FS approaches can be divided into three main classes: wrapperbased methods, filter-based methods, and embedded methods [1], [29], [30].…”

Section: Data Pre-processing and Features Selectionmentioning

confidence: 99%

Deep convolutional neural network model for bad code smells detection based on oversampling method

Khleel

Nehéz²

2022

IJEECS

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Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.</span>

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“…Software defect prediction models are mainly constructed by features, so the selection of features will directly affect the defect prediction results. The feature selection [37][38][39][40] affects the performance of defect prediction by influencing the accuracy and generalization of the machine learning models. When the dataset has only a small or redundant number of features, the machine model is unable to learn the general pattern, a situation that can lead to insufficient or overfitting.…”

Section: Introductionmentioning

confidence: 99%

Software defect prediction based on counterfactual explanations

Zheng

Chen

et al. 2022

Preprint

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Software defect prediction is critical to ensure the software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. The existing defect prediction models are mainly divided into two categories: one is the models constructed based on the artificial statistical features and the other is the models constructed based on the semantic features. DP-CNN [29] is one of the best defect prediction models, because it combines both the artificial statistical features and semantic features, so its performance is greatly improved compared with traditional defect prediction models. In this paper, we are inspired by the DP-CNN model and make the following two improvements: firstly, we use a new network representation technique Struc2vec to mine the information existing between software modules, which specializes in learning node representations from structural identity and can further extract the structural features associated with defects. We let the DP-CNN model again incorporate the newly mined structural features. Then, we propose a feature selection method based on counterfactual explanations which preserves reasonable causal relationships between features and thus produces a better subset of features compared to traditional feature selection methods. We used it to optimize the artificial statistical features inside the DP-CNN model. Based on the above methods, we propose a new hybrid defect prediction model DPS-CNN-STR. We evaluate our model on six open-source projects in terms of F1-score in defect prediction. The experimental results show that DPS-CNN-STR improves the state-of-the-art method by 3.6% on average.

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Code smell detection using feature selection and stacking ensemble: An empirical investigation

Cited by 44 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

Deep convolutional neural network model for bad code smells detection based on oversampling method

Software defect prediction based on counterfactual explanations

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