A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method

Abstract

Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to using historical defect data to construct a relationship between software metrics and defects via diverse methodologies. Several prediction models, such as machine learning (ML) and deep learning (DL), have been developed and adopted to recognize software module defects, and many methodologies and frameworks have been presented. Class imbalance is one of the most challenging problems these models face in binary classification. However, When the distribution of classes is imbalanced, the accuracy may be high, but the models cannot recognize data instances in the minority class, leading to weak classifications. So far, little research has been done in the previous studies that address the problem of class imbalance in SDP. In this study, the data sampling method is introduced to address the class imbalance problem and improve the performance of ML models in SDP. The proposed approach is based on a convolutional neural network (CNN) and gated recurrent unit (GRU) combined with a synthetic minority oversampling technique plus the Tomek link (SMOTE Tomek) to predict software defects. To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, F-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), and mean square error (MSE). The experimental results showed that the proposed models predict the software defects more effectively on the balanced datasets than the original datasets, with an improvement of up to 19% for the CNN model and 24% for the GRU model in terms of AUC. We compared our proposed approach with existing SDP approaches based on several standard performance measures. The comparison results demonstrated that the proposed approach significantly outperforms existing state-of-the-art SDP approaches on most datasets.

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

“…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.…”

“…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.…”

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

“…Within the GRU network, the update gate assumes a crucial role in determining the extent to which past information should be retained and carried forward to the future. Conversely, the reset gate assists in determining the degree to which past information should be ignored or forgotten [22], [23]. The calculation of the update gate in the GRU network can be demonstrated by the (6).…”

“…In the realm of software engineering, metrics serve as objective and systematic measurements utilized to evaluate different attributes of a software system. These metrics entail assigning numerical values or symbols to specific properties of the software under examination [23]. By employing software metrics, one can collect data on the structural aspects of a software design, allowing for a comprehensive analysis and interpretation of the software's characteristics using statistical methods [3].…”

“…These metrics play a vital role in assessing software quality, identifying potential issues, and guiding the decision-making process during software development and maintenance. By leveraging such objective measurements, software engineers can make informed and data-driven choices to improve the overall quality and reliability of software systems [23]. A public unified bug dataset aggregates bug reports and related data from various projects, domains, and platforms, facilitating bug prediction research and defect analysis.…”