Thesis

Model checking is an algorithmic method for ascertaining correct behavior of a model by checking if the model adheres to its formal properties. This dissertation addresses two important sanity checks for increasing confidence in the outcome of model checking, viz.: coverage analysis (``Is the set of properties adequate to certify correctness of the model?'') and vacuity detection (``Does the model satisfy the properties for the right reasons?''). This dissertation proposes a novel and fast algorithm to compute coverage of properties and detect vacuity of properties. This dissertation also shows that vacuous properties enable computation of coarser approximate images. In particular, it shows that vacuous properties induce weaker interpolants. In addition. this dissertation relates the semantic notions of vacuity and notions of coverage proving that vacuity is stronger than coverage.