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AbstractWe present a method for the automated verification of temporal properties of infinite state systems. Our verification method is based on the specialization of constraint logic programs (CLP) and works in two phases: (1) in the first phase, a CLP specification of an infinite state system is specialized with respect to the initial state of the system and the temporal property to be verified, and (2) in the second phase, the specialized program is evaluated by using a bottom-up strategy. The effectiveness of the method strongly depends on the generalization strategy which is applied during the program specialization phase. We consider several generalization strategies obtained by combining techniques already known in the field of program analysis and program transformation, and we also introduce some new strategies. Then, through many verification experiments, we evaluate the effectiveness of the generalization strategies we have considered. Finally, we compare the implementation of our specialization-based verification method to other constraint-based model checking tools. The experimental results show that our method is competitive with the methods used by those other tools.

We present a method for verifying properties of imperative programs by using techniques based on the specialization of constraint logic programs (CLP). We consider a class of imperative programs with integer variables and we focus our attention on safety properties, stating that no error configuration can be reached from any initial configuration. We introduce a CLP program I that encodes the interpreter of the language and defines a predicate unsafe equivalent to the negation of the safety property to be verified. Then, we specialize the CLP program I with respect to the given imperative program and the given initial and error configurations, with the objective of deriving a new CLP program I_sp that either contains the fact unsafe (and in this case the imperative program is proved unsafe) or contains no clauses with head unsafe (and in this case the imperative program is proved safe). If I_sp enjoys neither of these properties, we iterate the specialization process with the objective of deriving a CLP program where we can prove unsafety or safety. During the various specializations we may apply different strategies for propagating information (either propagating forward from an initial configuration to an error configuration, or propagating backward from an error configuration to an initial configuration) and different operators (such as the widening and the convex hull operators) for generalizing predicate definitions. Each specialization step is guaranteed to terminate, but due to the undecidability of program safety, the iterated specialization process may not terminate. By an experimental evaluation carried out on a significant set of examples taken from the literature, we show that our method improves the precision of program verification with respect to state-of-the-art software model checkers

We present VeriMAP, a tool for the verification of C programs based on the transformation of constraint logic programs, also called constrained Horn clauses. VeriMAP makes use of Constraint Logic Programming (CLP) as a metalanguage for representing: (i) the operational semantics of the C language, (ii) the program, and (iii) the property to be verified. Satisfiability preserving transformations of the CLP representations are then applied for generating verification conditions and checking their satisfiability. VeriMAP has an interface with various solvers for reasoning about constraints that express the properties of the data (in particular, integers and arrays). Experimental results show that VeriMAP is competitive with respect to state-of-the-art tools for program verification.

We address the problem of verifying the satisfiability of Constrained Horn Clauses (CHCs) based on theories of inductively defined data structures, such as lists and trees. We propose a transformation technique whose objective is the removal of these data structures from CHCs, hence reducing their satisfiability to a satisfiability problem for CHCs on integers and booleans. We propose a transformation algorithm and identify a class of clauses where it always succeeds. We also consider an extension of that algorithm, which combines clause transformation with reasoning on integer constraints. Via an experimental evaluation we show that our technique greatly improves the effectiveness of applying the Z3 solver to CHCs. We also show that our verification technique based on CHC transformation followed by CHC solving, is competitive with respect to CHC solvers extended with induction.