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This paper deals with the problem of test generation for hybrid systems, which are systems with mixed discrete and continuous dynamics. In our previous work [9], we developed a coverage guided test generation algorithm, inspired by a probabilistic motion planning technique. The algorithm is guided via a process of sampling goal states, which indicate the directions to steer the system towards. In this paper, we pursue this work further and propose a method to enhance coverage quality by introducing a new notion of disparity. This notion is used to predict the situations where the goal states can not be `directly' reached. We then develop an adaptive sampling method which permits improving coverage quality. This method was implemented and successfully applied to a number of case studies in analog and mixed-signal circuits, a domain where hybrid systems can be used as an appropriate high level model.

Using Disparity to Enhance Test Generation for Hybrid Systems