Abstract. Preprocessing SAT instances can reduce their size considerably. We combine variable elimination with subsumption and selfsubsuming resolution, and show that these techniques not only shrink the formula further than previous preprocessing efforts based on variable elimination, but also decrease runtime of SAT solvers substantially for typical industrial SAT problems. We discuss critical implementation details that make the reduction procedure fast enough to be practical. 1

Abstract. We presentsKizzo, a system designed to evaluate and certify Quantified Boolean Formulas (QBFs) by means of propositional skolemization and symbolic reasoning. 1

Many automatic testing, analysis, and verification techniques for programs can be effectively reduced to a constraint-generation phase followed by a constraint-solving phase. This separation of concerns often leads to more effective and maintainable tools. The increasing efficiency of off-the-shelf constraint solvers makes this approach even more compelling. However, there are few effective and sufficiently expressive off-the-shelf solvers for string constraints generated by analysis techniques for string-manipulating programs. We designed and implemented Hampi, a solver for string constraints over fixed-size string variables. Hampi constraints express membership in regular languages and fixed-size context-free languages. Hampi constraints may contain context-free-language definitions, regular-language definitions and operations, and the membership predicate. Given a set of constraints, Hampi outputs a string that satisfies all the constraints, or reports that the constraints are unsatisfiable. Hampi is expressive and efficient, and can be successfully applied to testing and analysis of real programs. Our experiments use Hampi in: static and dynamic analyses for finding SQL injection vulnerabilities in Web applications; automated bug finding in C programs using systematic testing; and compare Hampi with another string solver. Hampi’s source code, documentation, and the experimental data are available at

Much recent work has gone into adapting techniques that were originally developed for SAT solving to QBF solving. In particular, QBF solvers are often based on SAT solvers. Most competitive QBF solvers are search-based. In this work we explore an alternative approach to QBF solving, based on symbolic quantifier elimination. We extend some recent symbolic approaches for SAT solving to symbolic QBF solving, using various decision-diagram formalisms such as OBDDs and ZDDs. In both approaches, QBF formulas are solved by eliminating all their quantifiers. Our first solver, QMRES, maintains a set of clauses represented by a ZDD and eliminates quantifiers via multi-resolution. Our second solver, QBDD, maintains a set of OBDDs, and eliminate quantifier by applying them to the underlying OBDDs. We compare our symbolic solvers to several competitive search-based solvers. We show that QBDD is not competitive, but QMRES compares favorably with search-based solvers on various benchmarks consisting of non-random formulas.

The QCSP + language we introduce extends the framework of Quantified Constraint Satisfaction Problems (QCSPs) by enabling us to neatly express restricted quantifications via a chain of nested CSPs to be interpreted as alternately conjuncted and disjuncted. Restricted quantifiers turn out to be a convenient solution to the crippling modeling issues we encounter in QCSP and—surprisingly— they help to reuse propagation technology and to prune the search space. Our QCSP + solver—which also handles arithmetic and global constraints— exhibits state-of-the-art performances. 1

Abstract. Software model checking problems generally contain two different types of non-determinism: 1) non-deterministically chosen values; 2) the choice of interleaving among threads. Most modern software model checkers can handle only one source of non-determinism efficiently, but not both. This paper describes a SAT-based model checker for asynchronous Boolean programs that handles both sources effectively. We address the first type of non-determinism with a form of symbolic execution and fix-point detection. We address the second source of non-determinism using a symbolic and dynamic partial-order reduction, which is implemented inside the SAT-solver’s case-splitting algorithm. The preliminary experimental results show that the new algorithm outperforms the existing software model checkers on large benchmarks. 1

Abstract. Binary clause reasoning has found some successful applications in SAT, and it is natural to investigate its use in various extensions of SAT. In this paper we investigate the use of binary clause reasoning in the context of solving Quantified Boolean Formulas (QBF). We develop a DPLL based QBF solver that employs extended binary clause reasoning (hyper-binary resolution) to infer new binary clauses both before and during search. These binary clauses are used to discover additional forced literals, as well as to perform equality reduction. Both of these transformations simplify the theory by removing one of its variables. When applied during DPLL search this stronger inference can offer significant decreases in the size of the search tree, but it can also be costly to apply. We are able to show empirically that despite the extra costs, binary clause reasoning can improve our ability to solve QBF. 1

We describe BDD-based decision procedures for the modal logic K. Our approach is inspired by the automata-theoretic approach, but we avoid explicit automata construction. Instead, we compute certain fixpoints of a set of types---which can be viewed as an on-the-fly emptiness of the automaton. We use BDDs to represent and manipulate such type sets, and investigate different kinds of representations as well as a "level-based" representation scheme. The latter turns out to speed up construction and reduce memory consumption considerably. We also study the effect of formula simplification on our decision procedures. To proof the viability of our approach, we compare our approach with a representative selection of other approaches, including a translation of to QBF. Our results indicate that the BDD-based approach dominates for modally heavy formulae, while search-based approaches dominate for propositionally heavy formulae.

Symbolic model checking is PSPACE complete. Since QBF is the standard PSPACE complete problem, it is most natural to encode symbolic model checking problems as QBF formulas and then use QBF decision procedures to solve them. We discuss alternative encodings for unbounded and bounded safety checking into SAT and QBF. One contribution is a linear encoding of simple path constraints, which usually are necessary to make k-induction complete. Our experimental results show that indeed a large reduction in the size of the generated formulas can be obtained. However, current QBF solvers seem not to be able to take advantage of these compact formulations. Despite these mostly negative results the availability of these benchmarks will help improve the state of the art of QBF solvers and make QBF based symbolic model checking a viable alternative.

In recent years we have seen significant progress in the area of Boolean satisfiability (SAT) solving and its applications. As a new challenge, the community is now moving to investigate whether similar advances can be made in the use of Quantified Boolean Formulas (QBF). QBF provides a natural framework for capturing problem solving and planning in multiagent settings. However, contrarily to single-agent planning, which can be effectively formulated as SAT, we show that a QBF approach to planning in a multi-agent setting leads to significant unexpected computational difficulties. We identify as a key difficulty of the QBF approach the fact that QBF solvers often end up exploring a much larger search space than the natural search space of the original problem. This is in contrast to the experience with SAT approaches. We also show how one can alleviate these problems by introducing two special QBF formulations and a new QBF solution strategy. We present experiments that show the effectiveness of our approach in terms of a significant improvement in performance compared to earlier work in this area. Our work also provides a general methodology for formulating adversarial scenarios in QBF.