13 research outputs found
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
Template-Based Unbounded Time Verification of Affine Hybrid Automata
Computing over-approximations of all possible time trajectories is an important task in the analysis of hybrid systems. Sankaranarayanan et al. [20] suggested to approximate the set of reachable states using template polyhedra. In the present paper, we use a max-strategy improvement algorithm for computing an abstract semantics for affine hybrid automata that is based on template polyhedra and safely over-approximates the concrete semantics. Based on our formulation, we show that the corresponding abstract reachability problem is in co−NP. Moreover, we obtain a polynomial-time algorithm for the time elapse operation over template polyhedra
Speeding Up logico-numerical strategy iteration
We introduce an efficient combination of polyhedral analysis and predicate partitioning. Template polyhedral analysis abstracts numerical variables inside a program by one polyhedron per control location, with a priori fixed directions for the faces. The strongest inductive invariant in such an abstract domain may be computed by a combination of strategy iteration and SMT solving. Unfortunately, the above approaches lead to unacceptable space and time costs if applied to a program whose control states have been partitioned according to predicates. We therefore propose a modification of the strategy iteration algorithm where the strategies are stored succinctly, and the linear programs to be solved at each iteration step are simplified according to an equivalence relation. We have implemented the technique in a prototype tool and we demonstrate on a series of examples that the approach performs significantly better than previous strategy iteration techniques
Catalytic properties of spinel-type mixed oxides in transesterification of vegetable oils
Cross-reactivity of guaiacol and propionic acid blends during hydrodeoxygenation over Ni-supported catalysts
Particle transport in graphene nanoribbon driven by ultrashort pulses
We study charge transport in a graphene zigzag nanoribbon driven by an external
time-periodic kicking potential. Using the exact solution of the time-dependent Dirac
equation with a delta-kick potential acting in each period, we study the time evolution of
the population transfer probability and the time-dependent optical conductivity. By
variation of the kicking parameters, the conductivity becomes widely tunable