Selected Papers of the Conference ''Computer Aided Verification 2005''

2005

Editors: Kousha Etessami, Sriram K. Rajamani


1. Linear Encodings of Bounded LTL Model Checking

Biere, Armin ; Heljanko, Keijo ; Junttila, Tommi ; Latvala, Timo ; Schuppan, Viktor.
We consider the problem of bounded model checking (BMC) for linear temporal logic (LTL). We present several efficient encodings that have size linear in the bound. Furthermore, we show how the encodings can be extended to LTL with past operators (PLTL). The generalised encoding is still of linear size, but cannot detect minimal length counterexamples. By using the virtual unrolling technique minimal length counterexamples can be captured, however, the size of the encoding is quadratic in the specification. We also extend virtual unrolling to Buchi automata, enabling them to accept minimal length counterexamples. Our BMC encodings can be made incremental in order to benefit from incremental SAT technology. With fairly small modifications the incremental encoding can be further enhanced with a termination check, allowing us to prove properties with BMC. Experiments clearly show that our new encodings improve performance of BMC considerably, particularly in the case of the […]

2. Predicate Abstraction with Under-approximation Refinement

Pasareanu, Corina S. ; Pelanek, Radek ; Visser, Willem.
We propose an abstraction-based model checking method which relies on refinement of an under-approximation of the feasible behaviors of the system under analysis. The method preserves errors to safety properties, since all analyzed behaviors are feasible by definition. The method does not require an abstract transition relation to be generated, but instead executes the concrete transitions while storing abstract versions of the concrete states, as specified by a set of abstraction predicates. For each explored transition the method checks, with the help of a theorem prover, whether there is any loss of precision introduced by abstraction. The results of these checks are used to decide termination or to refine the abstraction by generating new abstraction predicates. If the (possibly infinite) concrete system under analysis has a finite bisimulation quotient, then the method is guaranteed to eventually explore an equivalent finite bisimilar structure. We illustrate the application of […]

3. Predicate Abstraction via Symbolic Decision Procedures

Lahiri, Shuvendu K. ; Ball, Thomas ; Cook, Byron.
We present a new approach for performing predicate abstraction based on symbolic decision procedures. Intuitively, a symbolic decision procedure for a theory takes a set of predicates in the theory and symbolically executes a decision procedure on all the subsets over the set of predicates. The result of the symbolic decision procedure is a shared expression (represented by a directed acyclic graph) that implicitly represents the answer to a predicate abstraction query. We present symbolic decision procedures for the logic of Equality and Uninterpreted Functions (EUF) and Difference logic (DIFF) and show that these procedures run in pseudo-polynomial (rather than exponential) time. We then provide a method to construct symbolic decision procedures for simple mixed theories (including the two theories mentioned above) using an extension of the Nelson-Oppen combination method. We present preliminary evaluation of our Procedure on predicate abstraction benchmarks from device driver […]

4. Interpolant-Based Transition Relation Approximation

Jhala, Ranjit ; McMillan, Kenneth L..
In predicate abstraction, exact image computation is problematic, requiring in the worst case an exponential number of calls to a decision procedure. For this reason, software model checkers typically use a weak approximation of the image. This can result in a failure to prove a property, even given an adequate set of predicates. We present an interpolant-based method for strengthening the abstract transition relation in case of such failures. This approach guarantees convergence given an adequate set of predicates, without requiring an exact image computation. We show empirically that the method converges more rapidly than an earlier method based on counterexample analysis.