A Novel Approach for Software Defect Prediction using CNN and GRU Based on SMOTE Tomek Method

Abstract

Software defect prediction (SDP) plays an important role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to the methods that use historical defect data to build the relationship between software metrics and software defects. Several prediction models such as machine learning (ML), deep learning (DL) have been developed and adopted to recognize defect in software modules and many methodologies and frameworks have been presented. One of the most difficult problems that these models face in binary classification is the classes imbalance. When the distribution of classes is unbalanced, the accuracy may be high, but the model cannot recognize data instances in the minority class, this will lead to weak classifications. So far, few research have been done in the previous studies that address the problem of class imbalance in SDP. To address the class imbalance problem, we propose a novel SDP approach based on convolutional neural network (CNN) and gated recurrent unit (GRU) combined with synthetic minority oversampling technique plus Tomek link (SMOTE Tomek). To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets which obtained from the PROMISE repository and the experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), mean square error (MSE). The average accuracy of the proposed models on the original datasets were 89% for CNN and 87% for GRU, while the average accuracy of the proposed models on the balanced datasets were 94% for CNN and 92% for GRU. The results showed that the proposed models on the balanced datasets improves the average accuracy by 5% for both models compared to original datasets. This indicates the positive effects of combining ML techniques with data balancing methods on the performance of defect prediction regarding datasets with imbalanced class distributions.

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

“…32 Thus, to take the advantages of both imbalance techniques, in this research we utilized SMOTETomek. 33 SMOTETomek is a hybrid sampling technique that combines the oversampling (SMOTE) and undersampling (Tomek Links) method and has widely been acknowledged in many domains such as software defect prediction, 32 medical data (diabetes), 34 for balancing the skewed data. In other words, the key concept of using this algorithm is to combine SMOTE method as data sampling and Tomek link as data cleaning method proposed by Tomek 35 to address the issue of imbalance data set.…”

“… 30 Synthetic minority oversampling (SMOTE) 31 considers the minority class while in contrast random under-sampling considers the majority class to equalize the class distribution. 32 Thus, to take the advantages of both imbalance techniques, in this research we utilized SMOTETomek. 33 SMOTETomek is a hybrid sampling technique that combines the oversampling (SMOTE) and undersampling (Tomek Links) method and has widely been acknowledged in many domains such as software defect prediction, 32 medical data (diabetes), 34 for balancing the skewed data.…”

Ensemble Learning Method for Predicting Protein Submitochondrial Localization by Multi-type Feature Fusion

“…Thirdly, compared to other methods, evolutionary features achieve best results, but the corresponding feature dimensions are also relatively higher, especially for the M495 dataset. Fourthly, the predictive accuracy of New-All-2 is not only best but also has lower feature dimensions, outperforming the combined methods of Group1, Group2, and Group3: To deal with the problem of sample imbalance, four different sampling methods were employed for comparison-SMOTE [24], SMOTEENN [25], ADASYN [28], and SMOTETOMEK [29]. The results are presented in Table 7.…”