Tests on artificial data sets are important in order to systematically assess the merits and limits of learning algorithms. This requires that distributions over both the hypothesis space and the instance space be controlled, which, in turn, implies the definition of control parameters. This paper surveys the control parameters that have been used in the context of grammatical inference, and provides directions for further identification of relevant parameters.