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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.

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