generated step function

planning.tex

@@ -85,11 +85,11 @@ The algorithm tries to find a plan that modifies the global state until the stat
 This is done by using the model-based testing facility of Gast.
 
 \paragraph{Model-Based Testing}
-Model-based testing checks whether an implementation adheres to a specification by repeatedly giving generated lists of input to both the system under test (SUT) and a model.
-SUT and model must be state transformer functions of type $\textsc{State} \times \textsc{Input} \to \textsc{State} \times \textsc{Output}$.
+Model-based testing checks whether an implementation adheres to a specification by repeatedly giving generated lists of input to both the implementation under test (IUT) and a model.
+IUT and model must be state transformer functions of type $\textsc{State} \times \textsc{Input} \to \textsc{State} \times \textsc{Output}$.
 A model is a reference implementation that implements one particular aspect of the specification.
 Testing stops when either the search space is exhausted or a counterexample has been found.
-A counterexample is a list of inputs that causes the SUT and the model to yield different outputs.
+A counterexample is a list of inputs that causes the IUT and the model to yield different outputs.
 Once a counterexample has been found, Gast uses cycle detection and other heuristics to condense it as much as possible into a minimal counterexample.
 
 \paragraph{Model-Based Synthesis}
@@ -152,4 +152,23 @@ The effect of \name{MakeCoffee} removes those units from the state and adds one
 
 The data type definition of a state, together with the actions define a problem \emph{domain}.
 A concrete \emph{planning problem} is defined by an initial state and a goal predicate inside the domain.
-In order to employ Gast, we need to take these components and construct a SUT and a model, to which Gast can feed the generated streams of input.
+In order to employ Gast, we need to take these components and construct an IUT and a model, to which Gast can feed the generated streams of input.
+Given a list of action definitions, our preprocessor generates a stepping function that is suitable to be used as a model for Gast.
+
+\begin{example}
+(Making coffe, continued)
+An excerpt of the step function, generated from the coffee actions, looks as follows.
+\begin{CLEAN}
+  :: Action = AddBeans Int | AddWater Int | MakeCoffee
+  step :: (State -> Bool) State Action -> [(State, [Bool])]
+  step pGoal state (AddWater w)
+    |  (w) > (0)
+    && ((state.water) + (w)) <= (maxWater)
+    = [(newState, [pGoal newState])]
+  where
+    newState = { state
+      & water = (state.water) + (w)
+      }
+\end{CLEAN}
+\qed
+\end{example}