Test Strategy Generation Using Quantified CSPs

Testing is the process of stimulating a system with inputs in order to reveal hidden parts of the system state. We consider a variant of the testing problem that was put forward in the model-based diagnosis literature, and consists of finding input patterns that definitely discriminate between different constraint-based system models. We show that this problem can be framed as a game played between two opponents, and naturally lends itself towards a formulation in terms of quantified CSPs. This QCSP-based formulation is a starting point to extend testing to a new classes of practically relevant applications – namely, systems with limited controllability – where tests consist of stimulation strategies instead of simple input patterns.