On Evaluating and Constraining Assertions Using Conflicts in Absent Scenarios

Mining from simulation data has been introduced as an effective solution to assertion generation for the design under verification (DUV) in prior work. As the simulation data is inherently incomplete, the quality of mined assertions is to be measured. In this paper, we propose a Belief-failRate framework to evaluate the success possibility of mined assertions, by taking both occurrences of free variable assignments and conflicts of absent scenarios into consideration. Meanwhile, a success possibility guided assertion constraining method is given to improve the quality of generated assertions. Experimental results show that assertions with high ranking using the proposed Belief-failRate framework are likely to have high possibility to be true assertions. Furthermore, guided by the success possibility, the proposed assertion constraining procedure can find assertions that cover new design functionality.