Name
Abstract | ||
|---|---|---|
In formal verification, we verify that a system is correct with respect to a specification. Cases like antecedent failure can make a successful pass of the verification procedure meaningless. Vacuity detection can signal such "meaningless" passes of the specification, and indeed vacuity checks are now a standard component in many commercial model checkers. We address two dimensions of vacuity: the computational effort and the information that is given to the user. As for the first dimension, we present several preliminary vacuity checks that can be done without the design itself, which implies that some information can be found with a significantly smaller effort. As for the second dimension, we present algorithms for deriving three types of information that are not provided by standard vacuity checks, assuming M 驴 for a model M and property 驴: a) behaviors that are possibly missing from M (or wrongly restricted by the environment) b) the largest subset of occurrences of literals in 驴 that can be replaced with false simultaneously without falsifying 驴 in M, and finally c) the degree of responsibility of each occurrence of a literal in 驴 to its satisfaction in the model M, which can be seen as a fine-grain form of vacuity. The complexity of each of these problems is proven. Overall this extra information can lead to tighter specifications and more guidance for finding errors. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/MEMCOD.2007.371225 | MEMOCODE |
Keywords | Field | DocType |
extra information,computational effort,commercial model checker,vacuity detection,vacuity check,present algorithm,preliminary vacuity check,standard vacuity check,informative vacuity checks,formal verification,model m,formal specification,two dimensions | Computer science,Theoretical computer science,Formal specification,Verification procedure,Formal verification | Conference |
ISBN | Citations | PageRank |
1-4244-1050-9 | 15 | 0.63 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hana Chockler | 1 | 482 | 38.31 |
| Ofer Strichman | 2 | 1071 | 63.61 |