Uniform Selection of Feasible Paths as a Stochastic Constraint Problem

Automatic structural test data generation is a real challenge of software testing. Statistical structural testing has been proposed to address this problem. This testing method aims at building an input probability distribution to maximize the coverage of some structural criteria. Under the all paths testing objective, statistical structural testing aims at selecting each feasible path of the program with the same probability. In this paper, we propose to model a uniform selector of feasible paths as a stochastic constraint program. Stochastic constraint programming is an interesting framework which combines stochastic decision problem and constraint solving. This paper reports on the translation of uniform selection of feasible paths problem into a stochastic constraint problem. An implementation which uses the library PCC(FD) of SICStus Prolog designed for this problem is detailed. First experimentations, conducted over a few academic examples, show the interest of our approach.