Chiranjeevulu Reddy Kasaram

From Scripts to Intelligence: Building Self-Learning Test Automation Frameworks

Abstract:

Software testing is rapidly evolving from static, script-based automation to intelligent, adaptive systems. This keynote explores the concept of self-learning test automation frameworks that leverage data, feedback loops, and AI techniques to dynamically adapt to application changes, reduce maintenance overhead, and improve reliability. By integrating capabilities such as failure pattern recognition, adaptive test selection, and autonomous script maintenance into CI/CD pipelines, organizations can transform testing from a reactive process into a proactive, intelligent system. The session also highlights how this shift redefines the role of test engineers toward designing scalable, intelligent quality ecosystems.

Profile:

Chiranjeevulu Kasaram is a Software Development Engineer in Test V with over a decade of experience in building scalable automation frameworks for complex, enterprise-grade systems, with a growing focus on the intersection of artificial intelligence and quality engineering. His work centers on transforming traditional testing approaches into intelligent, data-driven systems that improve reliability, reduce manual effort, and accelerate software delivery.

In recent years, Chiran has been actively developing AI-driven solutions to enhance software validation processes. His work explores how contextual data retrieval combined with generative models can enable smarter test case generation, intelligent failure analysis, reasoning, learning, and improving over time. By integrating generative AI systems into testing workflows, he aims to create adaptive, self-learning frameworks that continuously evolve with changing application behavior. Through this work, he is contributing to a shift from static automation toward intelligent validation ecosystems that can proactively identify risks and optimize software quality at scale.