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Boosting Verification by Automatic Tuning of Decision Procedures
 SEVENTH INTERNATIONAL CONFERENCE ON FORMAL METHODS IN COMPUTERAIDED DESIGN
, 2007
"... Parameterized heuristics abound in computer aided design and verification, and manual tuning of the respective parameters is difficult and timeconsuming. Very recent results from the artificial intelligence (AI) community suggest that this tuning process can be automated, and that doing so can lead ..."
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Cited by 46 (30 self)
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Parameterized heuristics abound in computer aided design and verification, and manual tuning of the respective parameters is difficult and timeconsuming. Very recent results from the artificial intelligence (AI) community suggest that this tuning process can be automated, and that doing so can lead to significant performance improvements; furthermore, automated parameter optimization can provide valuable guidance during the development of heuristic algorithms. In this paper, we study how such an AI approach can improve a stateoftheart SAT solver for large, realworld bounded modelchecking and software verification instances. The resulting, automaticallyderived parameter settings yielded runtimes on average 4.5 times faster on bounded model checking instances and 500 times faster on software verification problems than extensive handtuning of the decision procedure. Furthermore, the availability of automatic tuning influenced the design of the solver, and the automaticallyderived parameter settings provided a deeper insight into the properties of problem instances.
Deciding QuantifierFree Presburger Formulas Using Finite Instantiation Based on Parameterized Solution Bounds
 In Proc. 19 th LICS. IEEE
, 2003
"... Given a formula # in quantifierfree Presburger arithmetic, it is well known that, if there is a satisfying solution to #, there is one whose size, measured in bits, is polynomially bounded in the size of #. In this paper, we consider a special class of quantifierfree Presburger formulas in which m ..."
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Cited by 34 (7 self)
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Given a formula # in quantifierfree Presburger arithmetic, it is well known that, if there is a satisfying solution to #, there is one whose size, measured in bits, is polynomially bounded in the size of #. In this paper, we consider a special class of quantifierfree Presburger formulas in which most linear constraints are separation (di#erencebound) constraints, and the nonseparation constraints are sparse. This class has been observed to commonly occur in software verification problems. We derive a new solution bound in terms of parameters characterizing the sparseness of linear constraints and the number of nonseparation constraints, in addition to traditional measures of formula size. In particular, the number of bits needed per integer variable is linear in the number of nonseparation constraints and logarithmic in the number and size of nonzero coe#cients in them, but is otherwise independent of the total number of linear constraints in the formula. The derived bound can be used in a decision procedure based on instantiating integer variables over a finite domain and translating the input quantifierfree Presburger formula to an equisatisfiable Boolean formula, which is then checked using a Boolean satisfiability solver. We present empirical evidence indicating that this method can greatly outperform other decision procedures.
AvatarSAT: An Autotuning Boolean SAT Solver
"... Abstract. We present AVATARSAT, a SAT solver that uses machinelearning classifiers to automatically tune the heuristics of an offtheshelf SAT solver on a perinstance basis. The classifiers use features of both the input and conflict clauses to select parameter settings for the solver’s tunable h ..."
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Abstract. We present AVATARSAT, a SAT solver that uses machinelearning classifiers to automatically tune the heuristics of an offtheshelf SAT solver on a perinstance basis. The classifiers use features of both the input and conflict clauses to select parameter settings for the solver’s tunable heuristics. On a randomly selected set of SAT problems chosen from the 2007 and 2008 SAT competitions, AVATARSAT is, on average, over two times faster than MINISAT based on the geometric mean speedup measure and 50 % faster based on the arithmetic mean speedup measure. Moreover, AVATARSAT is hundreds to thousands of times faster than MINISAT on many hard SAT instances and is never more than twenty times slower than MINISAT on any SAT instance. 1
considered for publication in Logical Methods in Computer Science DECIDING QUANTIFIERFREE PRESBURGER FORMULAS USING PARAMETERIZED SOLUTION BOUNDS
, 2005
"... ABSTRACT. Given a formula Φ in quantifierfree Presburger arithmetic, if there is a satisfying solution to Φ, there is one whose size, measured in bits, is polynomially bounded in the size of Φ. In this paper, we consider a special class of quantifierfree Presburger formulas in which most linear co ..."
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ABSTRACT. Given a formula Φ in quantifierfree Presburger arithmetic, if there is a satisfying solution to Φ, there is one whose size, measured in bits, is polynomially bounded in the size of Φ. In this paper, we consider a special class of quantifierfree Presburger formulas in which most linear constraints are difference (separation) constraints, and the nondifference constraints are sparse. This class has been observed to commonly occur in software verification. We derive a new solution bound in terms of parameters characterizing the sparseness of linear constraints and the number of nondifference constraints, in addition to traditional measures of formula size. In particular, we show that the number of bits needed per integer variable is linear in the number of nondifference constraints and logarithmic in the number and size of nonzero coefficients in them, but is otherwise independent of the total number of linear constraints in the formula. The derived bound can be used in a decision procedure based on instantiating integer variables over a finite domain and translating the input quantifierfree Presburger formula to an equisatisfiable Boolean formula, which is then checked using a Boolean satisfiability solver. In addition to our main theoretical result, we discuss several optimizations for deriving tighter bounds in practice. Empirical evidence indicates that our decision procedure can greatly outperform other decision procedures. 1.