Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Abstract

Software Bug Prediction (SBP) is an integral process to the software's success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces software costs. The integration of Machine Learning (ML) algorithms has significantly improved software bug prediction accuracy and concurrently reduced costs and resource utilization. Numerous studies have explored the impact of Hyperparameter Optimization on single classifiers, enhancing these models' overall performance in SBP analysis. Ensemble Learning (EL) approaches have also demonstrated increased model accuracy and performance on SBP datasets. This study proposes a novel learning model for predicting software bugs through the utilization of EL and tuning hyperparameters. The results are compared with single hypothesis learning models using the WEKA software. The dataset, collected by the National Aeronautics and Space Administration (NASA) U.S.A., comprises 10,885 instances with 20 attributes, including a classifier for defects in one of their coding projects. The findings indicate that EL models outperform single hypothesis learning models, and the proposed model's accuracy increases after optimization. Furthermore, the accuracy of the proposed model demonstrates improvement following the optimization process. These results underscore the efficacy of ensemble learning, coupled with hyperparameter optimization, as a viable approach for enhancing the predictive capabilities of software bug prediction models.