Optimizing Software Quality: Integrating Test Case Prioritization, Defect Prediction, and Resource Allocation Strategies

Abstract

Software quality assurance is essential for reliable and effective systems, especially in critical fields like healthcare and autonomous vehicles. Yet, limited resources, slow fault detection, and the growing complexity of software designs—such as modular and distributed setups—create tough challenges. This paper explores progress in four key areas: requirement-based test case prioritization, software defect prediction, reliability checks for component-based systems, and resource allocation strategies. We reviewed 35 studies from 1992 to 2021, comparing older methods with newer ones using machine learning and deep learning. Our work shows that smart prioritization catches 30% more faults early, defect prediction models hit precision scores of 0.88–0.92, and resource allocation cuts testing effort by 25% without losing coverage. Still, issues like scaling up, real-world testing, and linking these methods together need more work. This study points out these gaps and suggests a combined approach to bring prioritization, prediction, and allocation into one system, aiming to improve software quality for today’s demanding applications.