1. IFL2002 Madrid 1 a generic test-system Pieter Koopman, Artem Alimarine, Jan Tretmans, Rinus Plasmeijer Nijmegen, NL

2. IFL2002 Madrid 2 overview testing: why what will be tested how do we test  by an automatic system  generic testing by a generic system  automatic and systematic generation of test data research question: can we make such a system conclusions / related & future work

3. IFL2002 Madrid 3 why testing even in functional programming we make errors  even errors that the type system does not spot to improve quality and confidence the software proving is often too much work, or not feasible manual testing is much work (up to 50% of project cost) dull to be repeated after changes  make an automatic system  encourages writing specifications  makes testing easier, faster and more reliable

4. IFL2002 Madrid 4 what will be tested there is a rich palette of quality aspects  suitability: validating  obeying the specification: functional testing  efficiency ... we restrict ourselves to: obeying the specification, functional testing specification can be:  executable specification inefficient but obviously correct  reference implementation older version  relation between input and output ...

5. IFL2002 Madrid 5 specification specification by functions in Clean prop_DeMorgan :: Bool Bool -> Bool prop_DeMorgan x y = x && y == not (not x || not y) semantics:  x,  y x && y == not (not x || not y) prop_DropDrop :: Int Int [Int] -> Bool prop_DropDrop m n xs = drop n (drop m xs) == drop (n+m) xs prop_Sqrt :: Real -> Property prop_Sqrt x = x>=0.0 ==> let r = sqrt x in r*r == x monomorph in order to generate test data  for polymorphic functions this tests all types  for overloaded functions the instance does matter

6. IFL2002 Madrid 6 how: basic idea generate test data, finite and   check property for all these values test prop | and [ evaluate prop x \\ x <- generate ] = "Passed" = "Counter-example found" requirements  one generate for all types  information about the counter-example  information about test data used what test data should be generated ?

7. IFL2002 Madrid 7 what test data? random ? (as in QuickCheck by Claessen & Hughes) + easy to implement + easy to use + works pretty well for small programs - are all relevant cases covered? - duplicates (which are useless) systematic ! + coverage of standard cases (guaranteed) Int: 0, 1,... List Int: [ ], [ 0 ], [ 1 ],... + no duplicates + more confidence, better results - harder to implement

8. IFL2002 Madrid 8 systematic generation of test data what do we want?  finite types :: Bool = True | False :: Colour = Red | Yellow | Blue –all values can and should be tested  very large types (always basic types) Int, Real, Char, String,... –common border values (like 0, 1 ) and random values  recursive types List a = Nil | Cons a (List a) Tree a = Tip | Node a (Tree a) (Tree a) –always handled by recursive functions –systematic generation from small to large

9. IFL2002 Madrid 9 systematic generation 2 basic types (Int, Real,...) are handled separately for all other types systematic generation from small to large is better than random  covers interesting cases  avoids duplicates how to define one general generate function ?  one function for all types  no new instance for each new type  systematic generation –no duplicates –small to large use generics !

10. IFL2002 Madrid 10 what are generics uniform tree representation of types conversion between actual type and tree representation by system tree representation can be manipulated in generic functions we use standard generics  See Hinze, Alimarine,... enables us to define one generate function  systematic generation of generic trees  automatic conversion of trees to needed types

11. IFL2002 Madrid 11 generic trees :: UNIT= UNIT// tip of tree :: EITHER a b= LEFT a | RIGHT b// choice :: PAIR a b= PAIR a b// product type example: :: Colour = Red | Yellow | Blue generic representation of this type :: Colour° = EITHER UNIT (EITHER UNIT UNIT) generic representation of constructors Red° = LEFT UNIT Yellow° = RIGHT (LEFT UNIT) Blue° = RIGHT (RIGHT UNIT) Red° Yellow°Blue° tree representation of all colours

12. IFL2002 Madrid 12 generic trees 2 representation of [] (which is Nil ) : LEFT UNIT note: same representation as Red! representation of [Red] ( Cons Red Nil ): RIGHT (PAIR (LEFT UNIT) (LEFT UNIT)) [] Cons Red YellowBlue... [] Cons... tree of all possible values of type [ Colour° ]°

13. IFL2002 Madrid 13 generic functions Basic idea:  instead of a function F :: A -> B for each A and B  we define a generic function F°  we implement F as GenToB o F° o AToGen  transformations are generated by the compiler AB F A°B° F° toGenfromGen

14. IFL2002 Madrid 14 Example a generic equality generic eq a :: a a -> Bool instances for generic type: eq{|UNIT|} UNIT UNIT = True eq{|PAIR|} eqx eqy (PAIR x1 y1) (PAIR x2 y2) = eqx x1 x2 && eqy y1 y2 eq{|EITHER|} eql eqr (LEFT x) (LEFT y) = eql x y eq{|EITHER|} eql eqr (RIGHT x) (RIGHT y) = eqr x y eq{|EITHER|} eql eqr e1 e2 = False eq{|Int|} n m = n == m // similar for other basic types for each type T we either use the generic version: derive eq T or define an own instance: eq{|T|} t1 t2 =... TTBool eq T°T° T°T° Bool eq

15. IFL2002 Madrid 15 function generate using generics we can define one generate function algorithm: systematic generation of generic trees automatic conversion to desired type

16. IFL2002 Madrid 16 systematic generation of generic trees preorder traversal  remember current tree –first all subtrees starting with LEFT –then all subtrees starting with RIGHT works fine for Colour, [ Colour],... problems if left generic subtree is infinite  Tree a = Tip | Node a (Tree a) (Tree a) using :: T = C Tree T generates : Tip, Node C Tip Tip, Node C (Node C Tip Tip) Tip, Node C (Node C (Node C Tip Tip) Tip) Tip,... never generates Node C Tip (Node C Tip Tip),...  [[T]] generates : [ ], [[ ]], [[C]], [[C, C]], [[C, C, C]],... never generates [[ ], [ ]], [[ ], [C]], [[C], [ ]],...

17. IFL2002 Madrid 17 generation of generic trees 2 breadth first traversal  order trees with respect to number of LEFT/RIGHT nodes, or constructors  yield trees with increasing depth  within same depth: left to right problems  not easy to generate trees with increasing depth efficiently –not every tree represents a valid data value –adding a constructor requires adding its arguments  large administration needed

18. IFL2002 Madrid 18 generation of generic trees 3 preorder does not work with left recursion breath first is nasty use mixed approach  remember generated branches of tree  extend tree for next test-data item  at each EITHER node choose randomly between extending LEFT or RIGHT branch Properties:  systematic  no duplicates  interesting cases occur soon (with high probability)  also bigger values earlier in list of test-data

19. IFL2002 Madrid 19 testing administrate arguments used in a record :: Result = { ok :: Maybe Bool, args :: [String] } define class Testable: class Testable a where evaluate :: a RandomStream Result -> [Result] instance Testable Bool where evaluate b rs result = [ {result & ok = Just b} ] instance Testable (a->b) | Testable b & TestArg a where evaluate f rs result = let (rs, rs2) = split rs in forAll rs2 f result (generate rs) forAll rs f r list = diagonal [apply (genRand seed) f a r \\ a<-list & seed <- rs ] apply rs f a r = evaluate (f a) rs {r & args = [show a:r.args]} use generic functions generate and show

20. IFL2002 Madrid 20 conditional tests prop_Sqrt :: Real -> Property prop_Sqrt x = x>=0.0 ==> let r = sqrt x in r*r == x implementation :: Property = Prop (RandomStream Result -> [Result]) (==>) infixr 0 :: Bool p -> Property | Testable p (==>) b p | b = Prop (evaluate p) // continue testing = Prop (\rs r = [r]) // stop testing, dummy result instance Testable Property where evaluate (Prop p) rs result = p rs result

21. IFL2002 Madrid 21 example results prop_Sqrt :: Real -> Property prop_Sqrt x = x>=0.0 ==> let r = sqrt x in r*r == x counter-example found after 2 tests:.1191576 prop_DropDrop :: Int Int [Int] -> Bool prop_DropDrop m n xs = drop n (drop m xs) == drop (n+m) xs counter-example found after 13 tests: -1 1 [0,0] prop_RevRev :: [Int] -> Property prop_RevRev xs = classify (isEmpty xs) xs ( reverse (reverse xs) == xs ) Passed 1000 tests []: 1 (0.1%) prop_DeMorgan :: Bool Bool -> Bool prop_DeMorgan x y = x && y == not (not x || not y) counter-example found after 3 tests: False True with correction specification: Passed 4 tests

22. IFL2002 Madrid 22 conclusions a test system is useful  writing specifications is encouraged  testing improves quality and confidence a generic test system is useful  property is arbitrary Clean function  system applies predicate to test-data  system generates test-data automatically  use generics to generate, show and compare values systematic generation of test data  no duplicated tests  covers interesting cases  stops if all cases are tested

23. IFL2002 Madrid 23 related work for FPL: QuickCheck advantages of our generic approach  one generate function for all types –no user defined instances needed »generate »show »equal  systematic generation of data –no duplicates –covers interesting cases –stops when all cases are tested

24. IFL2002 Madrid 24 future work handling  generating better functions prop_Map :: (Int->Int) (Int->Int) [Int] -> Bool prop_Map f g xs = map f (map g xs) == map (f o g) xs easier user control over test-data better info about test-data used case studies GUI testing application specified in Clean but written in some other programming language integration with proof-system...