Nareshkumar Soundarajan

AI-Augmented Quality Engineering for Modern Software Systems

Abstract:

The rapid evolution of modern software systems—driven by microservices, cloud-native architectures, and continuous delivery pipelines—has significantly increased the complexity of quality engineering. Traditional testing approaches, which rely heavily on manual effort and static automation, are no longer sufficient to ensure reliability, scalability, and rapid release cycles. As systems grow more distributed and dynamic, there is a critical need to transform quality engineering into an intelligent, adaptive, and data-driven discipline. This keynote explores how Artificial Intelligence is redefining quality engineering through the concept of AI-augmented testing. By integrating machine learning techniques into the testing lifecycle, organizations can move beyond reactive validation toward proactive and predictive quality assurance. The session will highlight key applications such as intelligent test case generation, anomaly detection in test failures, automated root cause analysis, and synthetic data generation for improving test coverage and handling data scarcity challenges. Drawing from real-world enterprise experiences, the talk will present a practical framework for embedding AI capabilities into existing automation pipelines, enabling faster feedback loops, improved defect detection, and optimized test execution. It will also address key challenges, including data quality, model trust, explainability, and the balance between human expertise and machine-driven insights. This keynote aims to provide both strategic vision and practical guidance on how organizations can evolve their quality engineering practices to meet the demands of modern software systems. By leveraging AI-driven approaches, teams can significantly enhance testing efficiency, accelerate delivery, and build more resilient, reliable applications in an increasingly complex digital landscape.