Abstract. We present the w constraint combinator that models while loops in Constraint Programming. Embedded in a finite domain constraint solver, it allows programmers to develop non-trivial arithmetical relations using loops, exactly as in an imperative language style. The deduction capabilities of this combinator comes from abstract interpretation over the polyhedra abstract domain. This combinator has already demonstrated its utility in constraint-based verification and we argue that it also facilitates the rapid prototyping of arithmetic constraints (power, gcd, sum, ...).

Goal-oriented test data generation; Constraint Logic Programming; Static Single Assignment formInternational audienceAutomatic test data generation leads to the identification of input values on which a selected path or a selected branch is executed within a program (path-oriented vs goal-oriented methods). In both cases, several approaches based on constraint solving exist, but in the presence of pointer variables only path-oriented methods have been proposed. Pointers are responsible for the existence of conditional aliasing problems that usually provoke the failure of the goal-oriented test data generation process. In this paper, we propose an overall constraint-based method that exploits the results of an intraprocedural points-to analysis and provides two specific constraint combinators for automatically generating goal-oriented test data. This approach correctly handles multi-levels stack-directed pointers that are mainly used in C programs. The method has been fully implemented in the test data generation tool INKA and first experiences in applying it to a variety of existing programs are presented

Constraint-Based Testing (CBT) is the process of generating test cases against a testing objective by using constraint solving techniques. In CBT, testing objectives are given under the form of properties to be satisfied by program's input/output. Whenever the program or the properties contain disjunctions or multiplications between variables, CBT faces the problem of solving non-linear constraint systems. Currently, existing CBT tools tackle this problem by exploiting a finite-domains constraint solver. But, solving a non-linear constraint system over finite domains is NP hard and CBT tools fail to handle properly most properties to be tested. In this paper, we present a CBT approach where a finite domain constraint solver is enhanced by Dynamic Linear Relaxations (DLRs). DLRs are based on linear abstractions derived during the constraint solving process. They dramatically increase the solving capabilities of the solver in the presence of non-linear constraints without compromising the completeness or soundness of the overall CBT process. We implemented DLRs within the CBT tool TAUPO that generates test data for programs written in C. The approach has been validated on difficult non-linear properties over a few (academic) C programs.