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Abstract. We present a new counterexample-guided abstraction refinement scheme. The scheme refines an over-approximation of the set of possible traces. Each refinement step introduces a finite automaton that recognizes a set of infeasible traces. A central idea enabling our approach is to use interpolants (assertions generated, e.g., by the infeasibility proof for an error trace) in order to automatically construct such an automaton. A data base of interpolant automata has an interesting potential for reuse of theorem proving work (from one program to another).

It is commonly understood that a verification tool should provide a counterexample to witness a specification violation. Until recently, software verifiers dumped error witnesses in proprietary formats, which are often neither human-nor machine-readable, and an exchange of witnesses between different verifiers was impossible. To close this gap in softwareverification technology, we have defined an exchange format for error witnesses that is easy to write and read by verification tools (for further processing, e.g., witness validation) and that is easy to convert into visualizations that conveniently let developers inspect an error path. To eliminate manual inspection of false alarms, we develop the notion of stepwise testification: in a first step, a verifier finds a problematic program path and, in addition to the verification result false, constructs a witness for this path; in the next step, another verifier re-verifies that the witness indeed violates the specification. This process can have more than two steps, each reducing the state space around the error path, making it easier to validate the witness in a later step. An obvious application for testification is the setting where we have two verifiers: one that is efficient but imprecise and another one that is precise but expensive. We have implemented the technique of error-witness-driven program analysis in two state-of-the-art verification tools, CPAchecker and Ultimate Automizer, and show by experimental evaluation that the approach is applicable to a large set of verification tasks.

In this expository paper, we use automata for software model checking in a new way. The starting point is to fix the alphabet: the set of statements of the given program. We show how automata over the alphabet of statements can help to decompose the main problem in software model checking, which is to find the right abstraction of a program for a given correctness property.

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