A Bit-Vector Solver with Word-Level Propagation

Wang, Wenxi; Søndergaard, Harald; Stuckey, Peter J.

doi:10.1007/978-3-319-33954-2_27

Cited by 7 publications

(18 citation statements)

References 5 publications

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“…This paper is mostly a distillation of a masters thesis [68]; it extends a previous paper [69], the focus of which was the idea of word-level propagation in a bit-vector solver. In this previous work we:…”

Section: Introductionmentioning

confidence: 92%

“…The approach is general, so we simply illustrate it via an example. Full details can be found elsewhere [68].…”

Section: Basic Propagatorsmentioning

confidence: 99%

“…Propagation rules and explanations for all the remaining logical constraints can be found elsewhere [68].…”

Section: ⊓ ⊔mentioning

confidence: 99%

“…We also give the specific decomposition rules we use for non-linear arithmetic constraints. The decomposition rules for the linear arithmetic constraints including addition, unary minus and subtraction constraints can be found elsewhere [69].…”

Section: Decomposed Propagatorsmentioning

confidence: 99%

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Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

2018

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We develop an idea originally proposed by Michel and Van Hentenryck of how to perform bit-vector constraint propagation on the word level. Most operations are propagated in constant time, assuming the bit-vector fits in a machine word. In contrast, bit-vector SMT solvers usually solve bitvector problems by (ultimately) bit-blasting, that is, mapping the resulting operations to conjunctive normal form clauses, and using SAT technology to solve them. Bit-blasting generates intermediate variables which can be an advantage, as these can be searched on and learnt about. As each approach has advantages, it makes sense to try to combine them. In this paper, we describe an approach to bit-vector solving using word-level propagation with learning. We have designed alternative word-level propagators to Michel and Van Hentenryck's, and evaluated different variants of the approach. We have also experimented with different approaches to learning and back-jumping in the solver. Based on the insights gained, we have built a portfolio solver, Wombit, which essentially extends the STP bit-vector solver. Using machine learning techniques, the solver makes a judicious up-front decision about whether to use word-level propagation or fall back on bit-blasting.

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Section: Introductionmentioning

confidence: 92%

“…The approach is general, so we simply illustrate it via an example. Full details can be found elsewhere [68].…”

Section: Basic Propagatorsmentioning

confidence: 99%

“…Propagation rules and explanations for all the remaining logical constraints can be found elsewhere [68].…”

Section: ⊓ ⊔mentioning

confidence: 99%

Section: Decomposed Propagatorsmentioning

confidence: 99%

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Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

2018

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“…Moreover, we perform an extensive comparison with SMT solvers on the whole SMTCOMP benchmark, while these previous efforts were either limited to conjunctive formulas or remain only theoretical. The results by Michel et al have been applied to a certain extent [46] as an extension of MiniSat [21], yet with no reduced product, to a limited set of BV operations and on BV sizes no larger than 64 bits. Older word-level approaches consider straightforward translations of bit-vector problems into disjunctive or non-linear arithmetic problems [10,22,40,45,48,49] (including bitblasting-like transformation for logical bitwise operators), and then rely on standard methods from linear integer programming or CP.…”

Section: Comparison To State-of-the-art Smt Solversmentioning

confidence: 99%

Sharpening Constraint Programming Approaches for Bit-Vector Theory

Chihani

Marre

Bobot

et al. 2017

Integration of AI and OR Techniques in Constraint Programming

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We address the challenge of developing efficient Constraint Programming-based approaches for solving formulas over the quantifier-free fragment of the theory of bitvectors (BV), which is of paramount importance in software verification. We propose CP(BV), a highly efficient BV resolution technique built on carefully chosen anterior results sharpened with key original features such as thorough domain combination or dedicated labeling. Extensive experimental evaluations demonstrate that CP(BV) is much more efficient than previous similar attempts from the CP community, that it is indeed able to solve the majority of the standard verification benchmarks for bitvectors, and that it already complements the standard SMT approaches on several crucial (and industry-relevant) aspects, notably in terms of scalability w.r.t. bit-width, theory combination or intricate mix of non-linear arithmetic and bitwise operators. This work paves the way toward building competitive CP-based verification-oriented solvers. * Work partially funded by ANR under grants ANR-14-CE28-0020 and ANR-12-INSE-0002. The CP solver COLIBRI is generously sponsored by IRSN, the French nuclear safety agency.

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A Tolerant Algebraic Side-Channel Attack on AES Using CP

Liu

Cruz

et al. 2017

Lecture Notes in Computer Science

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A Bit-Vector Solver with Word-Level Propagation

Cited by 7 publications

References 5 publications

Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

Sharpening Constraint Programming Approaches for Bit-Vector Theory

A Tolerant Algebraic Side-Channel Attack on AES Using CP

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