Integrating Cardinality Constraints into Constraint Logic Programming with Sets

Formal reasoning about finite sets and cardinality is important for many applications, including software verification, where very often one needs to reason about the size of a given data structure. The Constraint Logic Programming tool $$\{ log\} $$ provides a decision procedure for deciding the satisfiability of formulas involving very general forms of finite sets, although it does not provide cardinality constraints. In this paper we adapt and integrate a decision procedure for a theory of finite sets with cardinality into $$\{ log\} $$ . The proposed solver is proved to be a decision procedure for its formulas. Besides, the new CLP instance is implemented as part of the $$\{ log\} $$ tool. In turn, the implementation uses Howe and King’s Prolog SAT solver and Prolog’s CLP(Q) library, as an integer linear programming solver. The empirical evaluation of this implementation based on +250 real verification conditions shows that it can be useful in practice. Under consideration in Theory and Practice of Logic Programming (TPLP)

Selective Unification in (Constraint) Logic Programming*

Concolic testing is a well-known validation technique for imperative and object oriented programs. In a previous paper, we have introduced an adaptation of this technique to logic programming. At the heart of our framework lies a specific procedure that we call “selective unification”. It is used to generate appropriate run-time goals by considering all possible ways an atom can unify with the heads of some program clauses. In this paper, we show that the existing algorithm for selective unification is not complete in the presence of non-linear atoms. We then prove soundness and completeness for a restricted version of the problem where some atoms are required to be linear. We also consider concolic testing in the context of constraint logic programming and extend the notion of selective unification accordingly.

Anti-unification in Constraint Logic Programming

Anti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that captures a maximal amount of shared structure. In this work we address the problem of anti-unification in CLP, where goals can be seen as unordered sets of atoms and/or constraints. We show that while the concept of a most specific generalization can easily be defined in this context, computing it becomes an NP-complete problem. We subsequently introduce a generalization algorithm that computes a well-defined abstraction whose computation can be bound to a polynomial execution time. Initial experiments show that even a naive implementation of our algorithm produces acceptable generalizations in an efficient way.