Most generative systems built in game development are parameter-driven, but the relationship between parameters and the output of the system is often unclear. This makes them frustrating to use for both experts and novices, and as a result generators are often filtered post-hoc, or tweaked through time-consuming trial and error. In this paper we introduce two analytical techniques: smoothness and codependence. We show how these features help analyse the impact of a parameter change on a generative system and suggest ways this could feed back into more intelligent tools that make working with procedural generators more precise and pleasant.
