The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In this paper, we provide a framework for automated test data synthesis to test black-box ML/DL models. We address an important challenge of generating realistic user-controllable data with model agnostic coverage criteria to test a varied set of properties, essentially to increase trust in machine learning models. We experimentally demonstrate the effectiveness of our technique.