Software model checking, as an undecidable problem, has three possible outcomes: (1) the program satisfies the specification, (2) the program does not satisfy the specification, and (3) the model checker fails. The third outcome usually manifests itself in a space-out, time-out, or one component of the verification tool giving up; in all of these failing cases, significant computation is performed by the verification tool before the failure, but no result is reported. We propose to reformulate the model-checking problem as follows, in order to have the verification tool report a summary of the performed work even in case of failure: given a program and a specification, the model checker returns a condition Ψ ---usually a state predicate--- such that the program satisfies the specification under the condition Ψ ---that is, as long as the program does not leave the states in which Ψ is satisfied. In our experiments, we investigated as one major application of conditional model checking the sequential combination of model checkers with information passing. We give the condition that one model checker produces, as input to a second conditional model checker, such that the verification problem for the second is restricted to the part of the state space that is not covered by the condition, i.e., the second model checker works on the problems that the first model checker could not solve. Our experiments demonstrate that repeated application of conditional model checkers, passing information from one model checker to the next, can significantly improve the verification results and performance, i.e., we can now verify programs that we could not verify before.

Several successful approaches to software verification are based on the construction and analysis of an abstract reachability tree (ART). The ART represents unwindings of the control-flow graph of the program. Traditionally, a transition of the ART represents a single block of the program, and therefore, we call this approach single-block encoding (SBE). SBE may result in a huge number of program paths to be explored, which constitutes a fundamental source of inefficiency. We propose a generalization of the approach, in which transitions of the ART represent larger portions of the program; we call this approach large-block encoding (LBE). LBE may reduce the number of paths to be explored up to exponentially. Within this framework, we also investigate symbolic representations: for representing abstract states, in addition to conjunctions as used in SBE, we investigate the use of arbitrary Boolean formulas; for computing abstract-successor states, in addition to Cartesian predicate abstraction as used in SBE, we investigate the use of Boolean predicate abstraction. The new encoding leverages the efficiency of state-of-the-art SMT solvers, which can symbolically compute abstract large-block successors. Our experiments on benchmark C programs show that the large-block encoding outperforms the single-block encoding.