The problem of sampling a large number of random solutions to SAT and SMT constraints is essential for constrained-random verification and testing. However, most current sampling techniques lack a problem-specific notion of coverage, considering only general goals such as uniform distribution. We have developed a new technique for coverage-guided sampling that allows the user to specify the desired coverage points in order to shape the distribution of solutions. Our tool GUIDEDSAMPLER can efficiently generate high-quality stimuli for constrained-random verification, by sampling solutions to a SMT constraint that also cover a large number of user-defined coverage classes.
MWSCAS
2019-10-25
2024-11-29