AI-driven test execution is helping organizations scale their software validation and verification efforts and keep pace with the speed of DevOps. However, this new level of test automation is not without additional infrastructure costs. AI testing bots tend to consume a significant amount of resources when using deep machine learning models to generate and execute test cases. And so a new problem has arisen: how do we scale AI-driven testing efficiently and reliably to support the needs of large enterprises? Join Patrick Alt as he describes an approach for running hundreds of AI testing...
Patrick Alt
Software Architect
test.ai
Patrick Alt is a Software Architect at test.ai, building AI-powered automation tools that help testers, developers, and business stakeholders accelerate their releases. His expertise focuses on designing automated testing tools, domain-specific languages, full-stack software development, and DevOps. Patrick has dual Master's degrees in Computer Science from the Georgia Institute of Technology and the University of Stuttgart. He is a part of the Artificial Intelligence for Software Testing Association, contributing to AI-driven software testing prototypes. He has published research articles in IEEE and ACM-sponsored conferences and workshops.