Testing AI Systems That Change Over Time
Modern software systems increasingly rely on AI-driven features such as recommendations, copilots, and automated decision-making. Unlike traditional software, these systems evolve over time as data changes and user behavior shifts, making them difficult to test using deterministic test cases alone. Many testing teams struggle with unpredictable outputs, flaky tests, and failures that only appear after deployment. In this session, Dr. Longe will address the challenge of testing AI-enabled systems that change over time and explain how testers can adapt familiar testing principles to these new behaviors. She will present a practical, tester-friendly framework that focuses on risk-based test design, representative data selection, validation of expected behavior ranges, and ongoing observation of AI outputs. Attendees will leave with a clear understanding of how to reason about AI system behavior, concrete examples of test scenarios for AI features, and a simple checklist they can apply to enhance reliability, transparency, and confidence in AI-enabled applications.
Dr. Lola Longe is a Lecturer of Practice in Practical AI and Intelligent Automation at Sam Houston State University, where she teaches applied AI concepts, ethics, and AI literacy for technical and non-technical audiences. Her work focuses on helping professionals understand how AI systems behave, where they can fail, and how to evaluate them responsibly in real-world settings. Lola bridges academic scholarship and applied practice, translating complex AI concepts into practical guidance that testing and quality teams can use immediately. She has worked with students and professionals across technology and business domains to improve confidence, quality, and trust in AI-enabled systems. Her speaking interests include AI and ML for testing, risk-based quality strategies, and practical approaches to evaluating AI systems that evolve over time.