Cross-Project Defect Prediction: Leveraging Knowledge Transfer for Improved Software Quality Assurance

This research paper explores cross-project defect prediction as a means to improve software quality assurance (SQA) practices. Traditionally within-project defect prediction methods face challenges due to limited training data and project-specific characteristics. In contrast, cross-project defect prediction leverages knowledge transfer from multiple projects to develop more robust and generalizable defect prediction models. The study investigates various knowledge transfer strategies, such as instance-based, feature-based, and model-based transfer, and conducts extensive experiments on diverse software repositories. The results demonstrate that knowledge transfer techniques outperform traditional methods, offering higher accuracy and improved generalization to unseen projects. The paper also analyzes key factors influencing cross-project defect prediction success, providing practical guidelines for real-world SQA applications. By enabling effective defect prediction, this research contributes to enhancing software quality and maintenance.