Ensemble-Based Machine Learning Algorithms Combined with Near Miss Method for Software Bug Prediction

Abstract

Abstract Software bug prediction (SBP) involves identifying or categorizing software modules likely to contain defects, utilizing underlying system properties such as software metrics. SBP plays a crucial role in enhancing software project quality and mitigating maintenance risks. Numerous machine learning (ML) algorithms have been developed to predict software bugs. Class imbalance poses a significant challenge for these algorithms, significantly impeding their effectiveness and resulting in imbalanced false-positive and false-negative outcomes. However, limited research has been conducted to specifically tackle the issue of class imbalance in the context of SBP. This study investigates the prediction performance of a homogeneous ensemble: Bagging, boosting, and voting classifiers (VC) methods combined with the under-sampling methods to address the class imbalance problem and improve the accuracy of SBP. Two ensembles are classified as bagging ensembles: decision tree (DT) and random forest (RF); two ensembles are classified as boosting ensembles: AdaBoost (AB) and gradient boosting (GB), while the DT, RF, K-Nearest Neighbours (K-NN), and support vector machine (SVM) are considered as VC. To establish the effectiveness of the proposed models, the experiments were conducted on the available benchmark datasets, which comprise five public datasets based on both class and file-level metrics. We compared and evaluated the performance of the proposed models according to several performance measures, namely accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC). The experimental findings demonstrated that the proposed models exhibit superior efficiency in predicting software bugs on balanced datasets compared to the original datasets, with an improvement of up to 11% accuracy for the class-level metrics and 10% for the file-level metrics. The results indicate that the use of data sampling techniques had a positive impact on the prediction accuracy of the presented models. We compared our proposed method with existing SBP methods based on several standard performance measures. The comparison outcomes revealed a significant superiority of our method over the prevailing state-of-the-art SBP methods across most datasets.

Topics & Concepts