53 research outputs found
Succinct Representations for Abstract Interpretation
Abstract interpretation techniques can be made more precise by distinguishing
paths inside loops, at the expense of possibly exponential complexity.
SMT-solving techniques and sparse representations of paths and sets of paths
avoid this pitfall. We improve previously proposed techniques for guided static
analysis and the generation of disjunctive invariants by combining them with
techniques for succinct representations of paths and symbolic representations
for transitions based on static single assignment. Because of the
non-monotonicity of the results of abstract interpretation with widening
operators, it is difficult to conclude that some abstraction is more precise
than another based on theoretical local precision results. We thus conducted
extensive comparisons between our new techniques and previous ones, on a
variety of open-source packages.Comment: Static analysis symposium (SAS), Deauville : France (2012
Enforcing Termination of Interprocedural Analysis
Interprocedural analysis by means of partial tabulation of summary functions
may not terminate when the same procedure is analyzed for infinitely many
abstract calling contexts or when the abstract domain has infinite strictly
ascending chains. As a remedy, we present a novel local solver for general
abstract equation systems, be they monotonic or not, and prove that this solver
fails to terminate only when infinitely many variables are encountered. We
clarify in which sense the computed results are sound. Moreover, we show that
interprocedural analysis performed by this novel local solver, is guaranteed to
terminate for all non-recursive programs --- irrespective of whether the
complete lattice is infinite or has infinite strictly ascending or descending
chains
Improving Strategies via SMT Solving
We consider the problem of computing numerical invariants of programs by
abstract interpretation. Our method eschews two traditional sources of
imprecision: (i) the use of widening operators for enforcing convergence within
a finite number of iterations (ii) the use of merge operations (often, convex
hulls) at the merge points of the control flow graph. It instead computes the
least inductive invariant expressible in the domain at a restricted set of
program points, and analyzes the rest of the code en bloc. We emphasize that we
compute this inductive invariant precisely. For that we extend the strategy
improvement algorithm of [Gawlitza and Seidl, 2007]. If we applied their method
directly, we would have to solve an exponentially sized system of abstract
semantic equations, resulting in memory exhaustion. Instead, we keep the system
implicit and discover strategy improvements using SAT modulo real linear
arithmetic (SMT). For evaluating strategies we use linear programming. Our
algorithm has low polynomial space complexity and performs for contrived
examples in the worst case exponentially many strategy improvement steps; this
is unsurprising, since we show that the associated abstract reachability
problem is Pi-p-2-complete
Logico-numerical max-strategy iteration
Strategy iteration methods are used for solving fixed point equations. It has been shown that they improve precision in static analysis based on abstract interpretation and template abstract domains, e.g. intervals, octagons or template polyhedra. However, they are limited to numerical programs. In this paper, we propose a method for applying max-strategy iteration to logico-numerical programs, i.e. programs with numerical and Boolean variables, without explicitly enumerating the Boolean state space. The method is optimal in the sense that it computes the least fixed point w.r.t. the abstract domain; in particular, it does not resort to widening. Moreover, we give experimental evidence about the efficiency and precision of the approach
Using Bounded Model Checking to Focus Fixpoint Iterations
Two classical sources of imprecision in static analysis by abstract
interpretation are widening and merge operations. Merge operations can be done
away by distinguishing paths, as in trace partitioning, at the expense of
enumerating an exponential number of paths. In this article, we describe how to
avoid such systematic exploration by focusing on a single path at a time,
designated by SMT-solving. Our method combines well with acceleration
techniques, thus doing away with widenings as well in some cases. We illustrate
it over the well-known domain of convex polyhedra
Molecular and functional properties of P2X receptors—recent progress and persisting challenges
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