Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature

To ensure software quality, software defect prediction plays a prominent role for the software developers and practitioners. Software defect prediction can assist us with distinguishing software defect modules and enhance the software quality. In present days, many supervised machine learning algorithms have proved their efficacy to identify defective modules. However, those are limited to prove their major significance due to the limitations such as the adaptation of parameters with the environment and complexity. So, it is important to develop a key methodology to improve the efficiency of the prediction module. In this paper, an ensemble learning technique called Bootstrap aggregating has been proposed for software defect prediction object-oriented modules. The proposed method's accuracy, recall, precision, F-measure, and AUC-ROC efficiency were compared to those of many qualified machine learning algorithms. Simulation results and performance comparison are evident that the proposed method outperformed well compared to other approaches.