A model counter for constraints over unbounded strings

LuuLoi,; Shinde, Shweta; SaxenaPrateek,; DemskyBrian,

doi:10.1145/2666356.2594331

Cited by 24 publications

(29 citation statements)

References 32 publications

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“…To improve performance, such methods use sophisticated heuristics and variance reduction techniques, such as stratified sampling in [6,52]. The second approach is based on reduction to model counting [23,24,43,44], either using off-the-shelf #SMT solvers or developing #SMT procedures on top of existing tools. Another recent approach is based on data flow analysis [14].…”

Section: Discussionmentioning

confidence: 99%

“…Existing techniques for #SMT either incorporate model counting primitives into propositional reasoning [5,44,63] or are based on enumerative combinatorics [24,40,43]. Typically, exact algorithms [24,40,44] are exponential in the worst case, whereas approximate algorithms [43,63] lack provable performance guarantees.…”

Section: Counting In Smtmentioning

confidence: 99%

“…Typically, exact algorithms [24,40,44] are exponential in the worst case, whereas approximate algorithms [43,63] lack provable performance guarantees. In contrast to exact counting techniques, our procedure is easily implementable and uses "for free" the sophisticated heuristics built in off-the-shelf SMT solvers.…”

Section: Counting In Smtmentioning

confidence: 99%

“…In particular, there is a growing need for model counting solvers; for example, questions in quantitative information flow and in static analysis of probabilistic programs are naturally cast as instances of model counting problems for appropriate logical theories [24,43,52].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Approximate counting in SMT and value estimation for probabilistic programs

2017

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#SMT, or model counting for logical theories, is a well-known hard problem that generalizes such tasks as counting the number of satisfying assignments to a Boolean formula and computing the volume of a polytope. In the realm of satisfiability modulo theories (SMT) there is a growing need for model counting solvers, coming from several application domains (quantitative information flow, static analysis of probabilistic programs). In this paper, we show a reduction from an approximate version of #SMT to SMT. We focus on the theories of integer arithmetic and linear real arithmetic. We propose model counting algorithms that provide approximate solutions with formal bounds on the approximation error. They run in polynomial time and make a polynomial number of queries to the SMT solver for the underlying theory, exploiting "for free" the sophisticated heuristics implemented within modern SMT solvers. We have implemented the algorithms and used them to solve the value problem for a model of loop-free probabilistic programs with nondeterminism.

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Section: Discussionmentioning

confidence: 99%

Section: Counting In Smtmentioning

confidence: 99%

Section: Counting In Smtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approximate counting in SMT and value estimation for probabilistic programs

2017

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“…The SMC tool [29] addresses constraints on strings. It counts model for constraints written in a string language expressive enough to model constraints arising from JavaScript applications and UNIX C utilities.…”

Section: Related Workmentioning

confidence: 99%

Model Counting for Complex Data Structures

Filieri

Frias

PăźSăźReanu

et al. 2015

Model Checking Software

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We extend recent approaches for calculating the probability of program behaviors, to allow model counting for complex data structures with numeric fields. We use symbolic execution with lazy initialization to compute the input structures leading to the occurrence of a target event, while keeping a symbolic representation of the constraints on the numeric data. Off-the-shelf model counting tools are used to count the solutions for numerical constraints and field bounds encoding data structure invariants are used to reduce the search space. The technique is implemented in the Symbolic PathFinder tool and evaluated on several complex data structures. Results show that the technique is much faster than an enumeration-based method that uses the Korat tool and also highlight the benefits of using the field bounds to speed up the analysis.

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ZaligVinder: A generic test framework for string solvers

Kulczynski

Manea

Nowotka

et al. 2021

J Software Evolu Process

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The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user's purpose. Firstly, there is a multitude of string solvers, each addressing essentially some subset of the general problem. Generally, the addressed fragments are relevant and well motivated, but the lack of comparisons between the existing tools on an equal set of benchmarks cannot go unnoticed, especially as a common framework to compare solvers seems to be missing. In this paper, we gather a set of relevant benchmarks and introduce our new benchmarking framework to address this purpose.

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A model counter for constraints over unbounded strings

Cited by 24 publications

References 32 publications

Approximate counting in SMT and value estimation for probabilistic programs

Approximate counting in SMT and value estimation for probabilistic programs

Model Counting for Complex Data Structures

ZaligVinder: A generic test framework for string solvers

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