5,550 research outputs found
Predicate Abstraction with Under-approximation Refinement
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 the approach for checking concurrent programs.Comment: 22 pages, 3 figures, accepted for publication in Logical Methods in
Computer Science journal (special issue CAV 2005
First-Order Logic Theorem Proving and Model Building via Approximation and Instantiation
In this paper we consider first-order logic theorem proving and model
building via approximation and instantiation. Given a clause set we propose its
approximation into a simplified clause set where satisfiability is decidable.
The approximation extends the signature and preserves unsatisfiability: if the
simplified clause set is satisfiable in some model, so is the original clause
set in the same model interpreted in the original signature. A refutation
generated by a decision procedure on the simplified clause set can then either
be lifted to a refutation in the original clause set, or it guides a refinement
excluding the previously found unliftable refutation. This way the approach is
refutationally complete. We do not step-wise lift refutations but conflicting
cores, finite unsatisfiable clause sets representing at least one refutation.
The approach is dual to many existing approaches in the literature because our
approximation preserves unsatisfiability
Interpolant-Based Transition Relation Approximation
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.Comment: Conference Version at CAV 2005. 17 Pages, 9 Figure
AbsSynthe: abstract synthesis from succinct safety specifications
In this paper, we describe a synthesis algorithm for safety specifications
described as circuits. Our algorithm is based on fixpoint computations,
abstraction and refinement, it uses binary decision diagrams as symbolic data
structure. We evaluate our tool on the benchmarks provided by the organizers of
the synthesis competition organized within the SYNT'14 workshop.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Probabilistic Program Abstractions
Abstraction is a fundamental tool for reasoning about complex systems.
Program abstraction has been utilized to great effect for analyzing
deterministic programs. At the heart of program abstraction is the relationship
between a concrete program, which is difficult to analyze, and an abstract
program, which is more tractable. Program abstractions, however, are typically
not probabilistic. We generalize non-deterministic program abstractions to
probabilistic program abstractions by explicitly quantifying the
non-deterministic choices. Our framework upgrades key definitions and
properties of abstractions to the probabilistic context. We also discuss
preliminary ideas for performing inference on probabilistic abstractions and
general probabilistic programs
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