Artificial Intelligence (AI) technologies are widely used in computer applications to perform tasks
such as monitoring, forecasting, recommending, predicting, and statistical reporting. They are
deployed in a variety of systems, including driverless vehicles, robot-controlled warehouses,
financial forecasting applications, and security enforcement and are increasingly integrated with
cloud/fog/edge computing, big data analytics, robotics, Internet-of-Things (IoT), mobile computing,
smart cities, smart homes, intelligent healthcare, and many more. Despite this dramatic progress, the
quality assurance of existing AI application development processes is still far from satisfactory, and
the demand for demonstrable levels of confidence in such systems is growing. Software testing is a
fundamental, effective, and recognized quality assurance method which has shown its
cost-effectiveness to ensure the reliability of many complex software systems. However, the
adaptation of software testing to the peculiarities of AI applications remains largely unexplored and
needs extensive research to be performed. On the other hand, the availability of AI technologies
provides an exciting opportunity to improve existing software testing processes, and recent years
have shown that machine learning, data mining, knowledge representation, constraint optimization,
planning, scheduling, multi-agent systems, etc. have real potential to positively impact software
testing. Recent years have seen a rapid growth of interest in testing AI applications as well as the
application of AI techniques to software testing. This conference provides an international forum
for researchers and practitioners to exchange novel research results, articulate the problems and
challenges from practices, deepen our understanding of the subject area with new theories,
methodologies, techniques, process models, impacts, etc., and improve the practices with new tools
and resources.