AN EMPIRICAL EXPLORATION OF ARTIFICIAL INTELLIGENCE FOR SOFTWARE DEFECT PREDICTION IN SOFTWARE ENGINEERING

Abstract

Artificial Intelligence (AI) is an important topic in software engineering not only for data analysis and pattern recognition, but for the opportunity of finding solutions to problems that may not have explicit rules or instructions. Reliable prediction methods are needed because we cannot prove that there are no defects in software. Deep learning and machine learning have been applied to software defect prediction in the attempt to generate valid software engineering practices since at least 1971. Avoiding safety-critical or expensive system failures can save lives and reduce the economic burden of maintaining systems by preventing failures in systems such as aviation software, medical devices, and autonomous vehicles. This thesis contributes to the field of software defect prediction by empirically evaluating the performance of various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, and a stacking classifier combining these models. The findings highlight the importance of model selection and feature engineering in achieving accurate predictions. We followed this with a stacking classifier that combines Logistic Regression, Random Forest, and Support Vector Machine (SVM) to see if that improved predictive performance. We compared our results with previous work and analyzed which features or attributes appeared to be effective in predicting defects. We end by discussing potential next steps for further research based on our work results.