An \emph{enumerative strategy} is a list of indices that will be used to produce a list of values of an enumerated type. \emph{Enumerative} generators are the image of the enumerative strategy mapped to an enumeration. This strategy'' abstracts the approach of selecting and ordering the values of an enumerated type. Strategies are a heuristic means of selecting values. The usefulness of the resulting generator will depend on the circumstances. Any list of Integers can be used as an enumerative strategy, so these can be combined or customized as required for a given test. \begin{code}
module Test.GenCheck.Generator.EnumStrat where

import System.Random (StdGen, randomR)
import Data.List (genericTake)

import Test.GenCheck.Base.Base(Count)

type EnumStrat = Count -> [Count]

\end{code} Exhaustive testing includes all of the elements of the type. The exhaustive strategy has been included primarily for completeness and convenience. Using an enumerative strategy to construct an exhaustive generator will be inefficient in general, so a non-enumerative generator might be a better choice. \begin{code}
exhaustG :: EnumStrat
exhaustG u | u > 0 = [1..u]
exhaustG _ | otherwise = []

\end{code} Random testing makes use of Haskell's Random module to provide infinite streams of uniformly distributed indices. It requires a seed on input. Random testing may generate many duplicate or clustered test cases, providing insufficient diversity in a test suite, and is more expensive to calculate than the other strategies. It is, however, a good tool for supplementing the other strategies by providing some deviation from the uniform intervals in the index selection. \begin{code}
randG :: StdGen -> EnumStrat
randG s = \cnt -> if cnt>=0 then genericTake cnt $gr s cnt else [] where gr t cnt = let (x,s') = (randomR (1,cnt) t) in x : (gr s' cnt)  \end{code} Create a uniform sampling of the types by partitioning the index interval into equal increments. The meaning of uniform is dependent on the structure and it's enumeration, so this is more of a heuristic means of sampling the type values, but it guarantees at least some coverage of the type. If only 2 or fewer samples are requested, just the top and bottom of the interval are chosen. \begin{code} uniform :: Int -> EnumStrat uniform n u | n > 2 && u > 1 = let s = (toRational u) / (toRational n) in if u > (toInteger n) then take (n+1)$ 1 : (map round (iterate ((+) s) s))
else exhaustG u
uniform _ u | u > 1 = [1,u]
uniform _ u | u == 1 = [1]
uniform _ _ | otherwise = []


\end{code} Extreme covers the boundaries of the range, then recursively splits the range and takes those boundaries. For example, |extreme (1,20) = [1,20,10,2,11,9,19,5,15,3,12,6,16,4,14,8,18,7,13,17]| The first two extreme binary trees will be the all left'' and ''all right'' branched trees, which is a good start for testing, but in general extreme indices may or may not map to boundary conditions in a particular structure. Extreme generators is best used for pulling a small number of cases in conjunction with random testing. It is not an efficient strategy to produce a large number of test cases, and may contain duplicate index entries. \begin{code}
extreme :: EnumStrat
extreme up | up <= 0 = []
extreme up | up == 1 = [1]
extreme up = interleave low high
where low = [(1::Integer) .. (up div 2)]
high = [(up - x + 1) | x <- low]

interleave :: [a] -> [a] -> [a]
interleave xs [] = xs
interleave [] ys = ys
interleave (x:xs) (y:ys) =  x : (y : interleave xs ys)

\end{code} \begin{code}
branch :: EnumStrat
branch up | up <= 0 = []
branch up | up == 1 = [1]
branch up           = xtrm (1,up) -- up > 1
where
xtrm (l, u) =
let dif = u - l
m = (dif div 2) + l
in if dif < 1 then [l]
else if dif < 2 then [l,u]
else l : u : m :
(if dif < 3 then []
else interleave (xtrm ((l+1), (m-1))) (xtrm ((m+1), (u-1))))


\end{code}