Copyright	(c) Levent Erkok
License	BSD3
Maintainer	erkokl@gmail.com
Stability	experimental
Safe Haskell	None
Language	Haskell2010

Documentation.SBV.Examples.ProofTools.BMC

Contents

System state
Encoding the problem
Examples

Description

A BMC example, showing how traditional state-transition reachability problems can be coded using SBV, using bounded model checking.

We imagine a system with two integer variables, x and y. At each iteration, we can either increment x by 2, or decrement y by 4.

Can we reach a state where x and y are the same starting from x=0 and y=10?

What if y starts at 11?

Synopsis

data S a = S {
- x :: a
- y :: a
}
problem :: Int -> (S SInteger -> SBool) -> IO (Either String (Int, [S Integer]))
ex1 :: IO (Either String (Int, [S Integer]))
ex2 :: IO (Either String (Int, [S Integer]))

System state

data S a Source #

System state, containing the two integers.

Constructors

S
Fields x :: a y :: a

Instances

Instances details

Functor S Source #
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods fmap :: (a -> b) -> S a -> S b # (<$) :: a -> S b -> S a #
Foldable S Source #
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods fold :: Monoid m => S m -> m # foldMap :: Monoid m => (a -> m) -> S a -> m # foldMap' :: Monoid m => (a -> m) -> S a -> m # foldr :: (a -> b -> b) -> b -> S a -> b # foldr' :: (a -> b -> b) -> b -> S a -> b # foldl :: (b -> a -> b) -> b -> S a -> b # foldl' :: (b -> a -> b) -> b -> S a -> b # foldr1 :: (a -> a -> a) -> S a -> a # foldl1 :: (a -> a -> a) -> S a -> a # toList :: S a -> [a] # null :: S a -> Bool # length :: S a -> Int # elem :: Eq a => a -> S a -> Bool # maximum :: Ord a => S a -> a # minimum :: Ord a => S a -> a # sum :: Num a => S a -> a # product :: Num a => S a -> a #
Traversable S Source #
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods traverse :: Applicative f => (a -> f b) -> S a -> f (S b) # sequenceA :: Applicative f => S (f a) -> f (S a) # mapM :: Monad m => (a -> m b) -> S a -> m (S b) # sequence :: Monad m => S (m a) -> m (S a) #
Fresh IO (S SInteger) Source #	`Fresh` instance for our state
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods fresh :: QueryT IO (S SInteger) Source #
Show a => Show (S a) Source #	Show the state as a pair
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods showsPrec :: Int -> S a -> ShowS # show :: S a -> String # showList :: [S a] -> ShowS #
EqSymbolic a => EqSymbolic (S a) Source #	Symbolic equality for `S`.
Instance details Defined in Documentation.SBV.Examples.ProofTools.BMC Methods (.==) :: S a -> S a -> SBool Source # (./=) :: S a -> S a -> SBool Source # (.===) :: S a -> S a -> SBool Source # (./==) :: S a -> S a -> SBool Source # distinct :: [S a] -> SBool Source # distinctExcept :: [S a] -> [S a] -> SBool Source # allEqual :: [S a] -> SBool Source # sElem :: S a -> [S a] -> SBool Source # sNotElem :: S a -> [S a] -> SBool Source #

Encoding the problem

problem :: Int -> (S SInteger -> SBool) -> IO (Either String (Int, [S Integer])) Source #

We parameterize over the initial state for different variations.

Examples

ex1 :: IO (Either String (Int, [S Integer])) Source #

Example 1: We start from x=0, y=10, and search up to depth 10. We have:

>>> ex1
BMC: Iteration: 0
BMC: Iteration: 1
BMC: Iteration: 2
BMC: Iteration: 3
BMC: Solution found at iteration 3
Right (3,[(0,10),(2,10),(2,6),(2,2)])

As expected, there's a solution in this case. Furthermore, since the BMC engine found a solution at depth 3, we also know that there is no solution at depths 0, 1, or 2; i.e., this is "a" shortest solution. (That is, it may not be unique, but there isn't a shorter sequence to get us to our goal.)

ex2 :: IO (Either String (Int, [S Integer])) Source #

Example 2: We start from x=0, y=11, and search up to depth 10. We have:

>>> ex2
BMC: Iteration: 0
BMC: Iteration: 1
BMC: Iteration: 2
BMC: Iteration: 3
BMC: Iteration: 4
BMC: Iteration: 5
BMC: Iteration: 6
BMC: Iteration: 7
BMC: Iteration: 8
BMC: Iteration: 9
Left "BMC limit of 10 reached"

As expected, there's no solution in this case. While SBV (and BMC) cannot establish that there is no solution at a larger depth, you can see that this will never be the case: In each step we do not change the parity of either variable. That is, x will remain even, and y will remain odd. So, there will never be a solution at any depth. This isn't the only way to see this result of course, but the point remains that BMC is just not capable of establishing inductive facts.