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.

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>

In the present study, multilayer perceptron (MLP) neural network and support vector regression (SVR) models were developed to assess the suitability of groundwater for drinking purposes in the northern Khartoum area, Sudan. The groundwater quality was evaluated by predicting the groundwater quality index (GWQI). GWQI is a statistical model that uses sub-indices and accumulation functions to reduce the dimensionality of groundwater quality data. In the first stage, GWQI was calculated using 11 physiochemical parameters collected from 20 groundwater wells. These parameters include pH, EC, TDS, TH, Cl−, SO4−2, NO3−, Ca+2, Mg+2, Na+, and HCO3−. The primary investigation confirmed that all parameters except for EC and NO3− are beyond the standard limits of the World Health Organization (WHO). The measured GWQI ranged from 21 to 396. As a result, groundwater samples were classified into three classes. The majority of the samples, roughly 75%, projected into the excellent water category; 20% were considered good water and 5% were classified as unsuitable. GWQI models are powerful tools in groundwater quality assessment; however, the computation is lengthy, time-consuming, and often associated with calculation errors. To overcome these limitations, this study applied artificial intelligence (AI) techniques to develop a reliable model for the prediction of GWQI by employing MLP neural network and SVR models. In this stage, the input data were the detected physiochemical parameters, and the output was the computed GWQI. The dataset was divided into two groups with a ratio of 80% to 20% for models training and validation. The predicted (AI) and actual (calculated GWQI) models were compared using four statistical criteria, namely, mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Based on the obtained values of the performance measures, the results revealed the robustness and efficiency of MLP and SVR models in modeling GWQI. Consequently, groundwater quality in the north Khartoum area is evaluated as suitable for human consumption except for BH 18, where highly mineralized water is observed. The developed approach is advantageous in groundwater quality evaluation and is recommended to be incorporated in groundwater quality modeling.

Software bugs are defects or faults in computer programs or systems that cause incorrect or unexpected operations. These negatively affect software quality, reliability, and maintenance cost; therefore many researchers have already built and developed several models for software bug prediction. Till now, a few works have been done which used machine learning techniques for software bug prediction. The aim of this paper is to present comprehensive study on machine learning techniques that were successfully used to predict software bug. Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets. We compared the results of our proposed models with those of the other studies. The results of this study demonstrated that our proposed models performed better than other models that used the same data sets. The evaluation process and the results of the study show that machine learning algorithms can be used effectively for prediction of bugs.

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.