Invisible Intelligence: Embedding AI/ML for Smarter, Faster Release Testing
This paper explores a practical approach to embedding powerful AI/ML capabilities directly into your existing release management workflows, transforming them into intelligent systems without costly procurement or disruptive integration projects. We demonstrate how AI/ML can function as seamless extensions of your current development and testing infrastructure, unlocking significant efficiency gains. This case study provides a pragmatic framework for incrementally weaving AI/ML into your release processes.
Discover our practical, experience-based approach to implementing AI/ML across three critical dimensions: predictive deployment risk assessment, intelligent test redundancy elimination, and automated test strategy generation. By harnessing the data you already possess - version control history, build logs, test results - we developed predictive models that proactively flag risks before they impact production.
Learn specific techniques, including applying NLP to requirements and code changes, to auto-generate targeted test plans that adapt to evolving architectures. We'll detail how clustering and classification algorithms pinpointed overlapping test coverage, dramatically reducing validation effort while upholding rigorous quality standards.
Vidhya Ranganathan
With 14 years in the quality domain, I began my career as a manual tester before developing a strong passion for automation and DevOps. I specialize in designing frameworks, curating processes, and building dedicated quality teams, having significantly helped several startups bootstrap and scale their quality operations. Currently, as a Quality Manager at Okta, I leverage my strategic approach and hands-on expertise to drive excellence in quality assurance.