Field-testing is costly and time-consuming, hence, simulation-based testing is becoming more and more important to validate autonomous systems. Since autonomous systems can be deployed in diverse environments, a significant amount of diversified test cases has to be created. TAF (Testing Automation Framework) is a test generation tool we developed to serve this purpose. It produces the test cases from a data model that specifies the virtual environments of interest. This paper presents a practitioner's view of the integration of TAF into simulation-based test platforms, through two industrial case studies. The first one is for testing an agricultural robot developed by Naïo Technologies, and the second one for a static perception system by SICK AG that surveils a road crossing to support connected vehicles with tracking data in complex urban scenarios. We report on our experience in the design of the data models, as well as in the automation of the execution, logging, and analysis of the generated tests. We conclude with lessons learned.