Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article examines the convergence of Artificial Intelligence and Agile methodologies in software development, focusing on how predictive Quality Assurance models are transforming traditional testing approaches. As development cycles accelerate and application complexity increases, conventional QA processes often become bottlenecks in the software delivery lifecycle. By leveraging historical test data, code metrics, and usage patterns, AI-enhanced QA can predict potential failure points, optimize test execution, and accelerate delivery cycles without compromising software quality. The integration creates a synergistic relationship where AI capabilities complement Agile principles, enabling more efficient test prioritization, suite optimization, anomaly detection, intelligent defect triage, and automated test generation. Through case studies from e-commerce and telecommunications sectors, the article presents implementation strategies, challenges, and outcomes, offering a structured framework for organizations seeking to adopt predictive QA within their Agile environments.