Leveraging Machine Learning for Accurate Defect Prediction in Software QA

Abstract

Software Quality Assurance (QA) has undergone significant transformations over the years, driven by advancements in methodologies, tools, and technologies. Among the most impactful innovations is the integration of machine learning (ML) techniques, which are revolutionizing the way defects are predicted, testing processes are optimized, and overall software reliability is ensured. This paper explores the evolution of QA, with a particular focus on how machine learning models especially those used for defect prediction are reshaping the future of quality assurance practices. By building on seminal research, including the foundational work of Kothamali and Banik, this paper demonstrates how the application of machine learning in QA is enhancing defect detection accuracy, optimizing resource allocation, and improving project risk management, ultimately leading to more efficient and reliable software development processes.