An approach for automatically generating loop invariants using quantifier-elimination is proposed. An invariant of a loop is hypothesized as a parameterized formula. Parameters in the invariant are discovered by generating constraints on the parameters by ensuring that the formula is indeed preserved by the execution path corresponding to every basic cycle of the loop. The parameterized formula can be successively refined by considering execution paths one by one; heuristics can be developed for determining the order in which the paths are considered. Initialization of program variables as well as the precondition and postcondition of the loop, if available, can also be used to further refine the hypothesized invariant. Constraints on param...
AbstractWe present an application of quantifier elimination techniques in the automatic parallelizat...
International audienceBy combining algorithmic learning, decision procedures, and predicate abstract...
Abstract. By combining algorithmic learning, decision procedures, and predicate abstraction, we pres...
An approach for automatically generating loop invariants using quantifier-elimination is proposed. A...
Abstract. Most of the properties established during program verification are either invariants or de...
Provably correct software is one of the key challenges in our software-driven society. Program verif...
Geometric heuristics for the quantifier elimination approach presented by Kapur (2004) are investiga...
We describe an iterative algorithm for mechanically deriving loop invariants for the purpose of prov...
A general framework is presented for automating the discovery of loop invariants for imperative prog...
We describe an iterative algorithm for mechanically deriving loop invariants for the purpose of prov...
International audienceBy combining algorithmic learning, decision procedures, predicate abstraction,...
AbstractIn the mechanical verification of programs containing loops it is often necessary to provide...
Automatically generating invariants, key to computer-aided analysis of probabilistic and determinist...
A general framework is presented for automatig the discovery of loop invariants for imperative progr...
Formal program verification faces two problems. The first problem is related to the necessity of hav...
AbstractWe present an application of quantifier elimination techniques in the automatic parallelizat...
International audienceBy combining algorithmic learning, decision procedures, and predicate abstract...
Abstract. By combining algorithmic learning, decision procedures, and predicate abstraction, we pres...
An approach for automatically generating loop invariants using quantifier-elimination is proposed. A...
Abstract. Most of the properties established during program verification are either invariants or de...
Provably correct software is one of the key challenges in our software-driven society. Program verif...
Geometric heuristics for the quantifier elimination approach presented by Kapur (2004) are investiga...
We describe an iterative algorithm for mechanically deriving loop invariants for the purpose of prov...
A general framework is presented for automating the discovery of loop invariants for imperative prog...
We describe an iterative algorithm for mechanically deriving loop invariants for the purpose of prov...
International audienceBy combining algorithmic learning, decision procedures, predicate abstraction,...
AbstractIn the mechanical verification of programs containing loops it is often necessary to provide...
Automatically generating invariants, key to computer-aided analysis of probabilistic and determinist...
A general framework is presented for automatig the discovery of loop invariants for imperative progr...
Formal program verification faces two problems. The first problem is related to the necessity of hav...
AbstractWe present an application of quantifier elimination techniques in the automatic parallelizat...
International audienceBy combining algorithmic learning, decision procedures, and predicate abstract...
Abstract. By combining algorithmic learning, decision procedures, and predicate abstraction, we pres...