  Openness and intelligence are two enabling features to be introduced in next
generation wireless networks, e.g. Beyond 5G and 6G, to support service
heterogeneity, open hardware, optimal resource utilization, and on-demand
service deployment. The open radio access network (O-RAN) is a promising RAN
architecture to achieve both openness and intelligence through virtualized
network elements and well-defined interfaces. While deploying artificial
intelligence (AI) models is becoming easier in O-RAN, one significant challenge
that has been long neglected is the comprehensive testing of their performance
in realistic environments. This article presents a general automated,
distributed and AI-enabled testing framework to test AI models deployed in
O-RAN in terms of their decision-making performance, vulnerability and
security. This framework adopts a master-actor architecture to manage a number
of end devices for distributed testing. More importantly, it leverages AI to
automatically and intelligently explore the decision space of AI models in
O-RAN. Both software simulation testing and software-defined radio hardware
testing are supported, enabling rapid proof of concept research and
experimental research on wireless research platforms.
