Autonomous Quality Engineering: Building AI Systems That Generate, Execute, Heal, Learn, and Govern Software Quality
The proposed framework uses Generative AI, LLMs, AI agents, machine learning models, and observability feedback to support self-healing test automation, LLM-based test generation, prompt regression testing, output quality scoring, predictive defect analytics, and intelligent release decisions — transforming quality engineering from reactive validation into an adaptive, intelligence-driven capability.
Problem statements this talk addresses:
Conventional testing is often reactive, manually maintained, and dependent on stable test data that rarely exists.
Automation suites struggle with dynamic catalog and pricing changes, personalized experiences, non-deterministic recommendations, A/B test variants, and payment-provider instability.
Customer-facing Gen AI features add new risks: hallucinated product details, incorrect policy guidance, unsafe responses, prompt drift, and inconsistent output after model or prompt changes.
Key Takeaways:
Enterprise e-commerce platforms require a quality engineering model that is adaptive, intelligent, and capable of managing constant change.
Generative AI and LLMs can improve test design, defect analysis, prompt validation, and autonomous decision support, but these capabilities must operate within strong governance, measurable quality indicators, and human oversight.
The proposed framework positions quality engineering as a strategic capability that supports faster delivery, better customer experience, lower operational risk, and greater release confidence across complex e-commerce ecosystems.
About the speaker
Jyotheeswara Reddy Gottam:
Jyotheeswara Gottam is a technology professional specializing in software quality engineering and modern testing practices with 15+ years experience. With a strong focus on innovation in test automation, Jyotheeswara has a keen interest in leveraging emerging technologies such as Generative AI, predictive analytics, and intelligent automation to improve software reliability and reduce operational costs. Currently working at Walmart, Jyotheeswara contributes to building scalable and efficient testing solutions for complex enterprise systems. Their work emphasizes the adoption of data-driven and AI-powered approaches to enhance test coverage, accelerate delivery cycles, and improve overall product quality. Jyotheeswara is particularly passionate about transforming traditional QA processes into intelligent, adaptive systems and actively explores advancements in self-healing automation and risk-based testing strategies. Through research and practical implementation, they aim to bridge the gap between cutting-edge AI capabilities and real-world software engineering challenges.
About
TestMu Conf
Testμ (TestMu) is the world’s largest virtual conference on agentic engineering and quality, built by the community, for the community. As AI reshapes how we build, test, and ship software, Testμ Conf is where you connect, grow, and lead: agentic workflows, autonomous quality, battle-tested AI playbooks, hands-on workshops, and the engineering culture driving it all.
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