Skip to Main Content
We present a mutation-based validation paradigm (MVP) technology that can handle complete high-level microprocessor implementations and is based on explicit design error modeling, design error simulation, and model-directed test vector generation. We first present a control-based coverage measure that is aimed at exposing design errors that incorrectly set control signal values. We then describe MVP's high-level concurrent design error simulator that can handle various modeled design errors. We then present fundamental techniques and data structures for analyzing high-level circuit implementations and present various optimizations to speed up the processing of data structures and consequently speed up MVP's overall test generation process. We next introduce a new automatic test vector generation technique for high-level hardware descriptions that generates a test sequence by efficiently solving constraints on multiple finite state machines. To speed up the test generation, MVP is empowered by learning abilities via profiling various aspects of the test generation process. Our experimental results show that MVP's learning abilities and automated test vector generation effectiveness make MVP significantly better than random or pseudorandom validation techniques.