Refinement Selection

Counterexample-guided abstraction refinement (CEGAR) is a property-directed approach for the automatic construction of an abstract model for a given system. The approach learns information from infeasible error paths in order to refine the abstract model. We address the problem of selecting which information to learn from a given infeasible error path. In previous work, we presented a method that enables refinement selection by extracting a set of sliced prefixes from a given infeasible error path, each of which represents a different reason for infeasibility of the error path and thus, a possible way to refine the abstract model. In this work, we (1) define and investigate several promising heuristics for selecting an appropriate precision for refinement, and (2) propose a new combination of a value analysis and a predicate analysis that does not only find out which information to learn from an infeasible error path, but automatically decides which analysis should be preferred for a refinement. These contributions allow a more systematic refinement strategy for CEGAR-based analyses. We evaluated the idea on software verification. We provide an implementation of the new concepts in the verification framework

and make it publicly available. In a thorough experimental study, we show that refinement selection often avoids state-space explosion where existing approaches diverge, and that it can be even more powerful if applied on a higher level, where it decides which analysis of a combination should be favored for a refinement.