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Wednesday, April 30, 2025 - 2:45pm to 3:45pm

From Vision to Velocity: Accelerating Agile Testing with Generative AI

In the rapidly evolving landscape of Agile and DevOps, traditional methods of testing business-facing features often struggle to keep pace with the demands for faster and more thorough testing. However, the fusion of traditional testing wisdom with cutting-edge AI presents a unique opportunity to enhance software quality and delivery speed, offering innovative solutions to longstanding challenges. Join Phillip as he examines key test design techniques - such as equivalence partitioning, boundary value analysis, decision table testing, and state transition testing - and explores how Generative AI tools can enhance these methods. From automating the identification of test conditions to optimizing test case coverage, AI can significantly improve the efficiency and effectiveness of your testing efforts. By strategically blending human expertise with AI capabilities, we will adapt these traditional techniques to create more comprehensive automated test suites. This session is tailored for testers and test managers who are seeking to optimize their current testing practices. You will gain fresh insights on harmonizing established testing methodologies with the latest AI advancements. Discover how AI can not only complement but also elevate your black box testing strategy, empowering you to meet the challenges of modern software testing head-on.

Philip Daye
Insider Intelligence

Philip Daye is a seasoned software quality professional with more than 25 years of experience in the field. Currently the QA Team Lead at Insider Intelligence, he has a diverse background as a tester, manager, architect, and leader, and has worked with companies of all sizes to ensure the delivery of high-quality software. Philip is deeply committed to staying current with advances in the field, and actively shares his knowledge and experience with others through speaking engagements at conferences and meetups, as well as by founding internal communities of practice.