12,603 research outputs found
Software Model Checking via Large-Block Encoding
The construction and analysis of an abstract reachability tree (ART) are the
basis for a successful method for software verification. 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.Comment: 13 pages (11 without cover), 4 figures, 5 table
An expectation transformer approach to predicate abstraction and data independence for probabilistic programs
In this paper we revisit the well-known technique of predicate abstraction to
characterise performance attributes of system models incorporating probability.
We recast the theory using expectation transformers, and identify transformer
properties which correspond to abstractions that yield nevertheless exact bound
on the performance of infinite state probabilistic systems. In addition, we
extend the developed technique to the special case of "data independent"
programs incorporating probability. Finally, we demonstrate the subtleness of
the extended technique by using the PRISM model checking tool to analyse an
infinite state protocol, obtaining exact bounds on its performance
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
On abstraction refinement for program analyses in Datalog
A central task for a program analysis concerns how to efficiently find a program abstraction that keeps only information relevant for proving properties of interest. We present a new approach for finding such abstractions for program analyses written in Datalog. Our approach is based on counterexample-guided abstraction refinement: when a Datalog analysis run fails using an abstraction, it seeks to generalize the cause of the failure to other abstractions, and pick a new abstraction that avoids a similar failure. Our solution uses a boolean satisfiability formulation that is general, complete, and optimal: it is independent of the Datalog solver, it generalizes the failure of an abstraction to as many other abstractions as possible, and it identifies the cheapest refined abstraction to try next. We show the performance of our approach on a pointer analysis and a typestate analysis, on eight real-world Java benchmark programs
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