Next-Generation Software Quality Assurance: Integrating AI-Driven Predictive Analytics, Digital Twins, and Agile Methodologies for Transformative Research and Practice

Abstract

Software Quality Assurance (SQA) sees a transformation as artificial intelligence (AI), predictive analytics, digital twin technologies, and Agile approaches converge to meet the requirements of contemporary software ecosystems. Conventional quality assurance has predominantly been reactive, emphasizing flaw identification at the conclusion of development cycles. Although beneficial, these methods are becoming insufficient for managing the complexity, speed, and essential nature of modern software systems, especially in industries like healthcare, banking, defense, and vital infrastructure. This article offers an in-depth examination of next-generation Software Quality Assurance (SQA), contending that predictive analytics facilitates proactive defect identification, digital twins allow for real-time simulation and validation, and Agile frameworks provide the cultural and organizational infrastructure essential for integrating these technologies into practice. The work utilizes recent literature contributions (Joy, Alam, & Bakhsh, 2024; Bakhsh, Joy, & Alam, 2024; Bakhsh, Alam, & Nadia, 2025; Alam et al., 2025; Gazi Touhidul Alam et al., 2025) with extensive research in AI, software engineering, and organizational science. A three-pillar framework is offered, with predictive analytics as the foundation, digital twins as the simulation engine, and Agile as the delivery technique. The essay highlights the ramifications for U.S. businesses and national objectives, especially for cybersecurity resilience, healthcare safety, and digital competitiveness. Future research avenues encompass explainable AI-driven question answering, cross-domain digital twin integration, and ethical implications for autonomous question answering bots.