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Let the weak say, I am strong. Or not.
Let the weak say, I am strong.
Or not.
When programming in functional languages such as Agda and Haskell, we usually define functions and then show that they satisfy a given specification. To that effect, we can
define functions with a weak specification and then show that they satisfy it via companion tests or proofs, or
define functions with a strong specification which they satisfy by definition.
As an example, let's consider the Functor
type class in Haskell,
which is used for types that can be mapped over:^{1}
class Functor f where
fmap :: (a -> b) -> f a -> f b
Instances of Functor
should satisfy the following laws:
fmap id == id
fmap (g . f) == fmap g . fmap f
Declaring an instance of the Functor
type class and then testing or
proving these laws is how we define a functor with a weak
specification in Haskell.
For example, this is the instance of Functor
for lists, which
satisfies the functor laws:
instance Functor [] where
fmap _ [] = []
fmap f (x:xs) = f x : fmap f xs
Certainly, declaring the instance of Functor
for lists is not
enough. We need to test or prove that it satisfies the functor laws,
but we're not going to do that yet.
In order to see that thinking of the functor laws is important, let's
consider an alternative instance of Functor
for lists:
instance Functor [] where
fmap _ _ = []
Even though this is a valid instance of Functor
, it's not really a
functor because it violates the functor laws. With a weak
specification, ensuring that the laws hold is our responsibility, but
we could use a strong specification in which we define a functor and,
at the same time, prove that it is indeed a functor.
Now, defining functions with a strong specification usually relies on dependent types or types that depend on elements of other types, which is why we'll take a look at (weak and strong) functors in Agda:
record RawFunctor (F : Set → Set) : Set₁ where
field
fmap : ∀ {A B} → (A → B) → F A → F B
Here, we use record types in place of Haskell type classes, so the
RawFunctor
record is equivalent to the Functor
type class in
Haskell.
Declaring a RawFunctor
record and then proving the functor laws is
how we define a functor with a weak specification in Agda. In fact,
this is how functors are defined in the Agda standard library right
now.
For example, this is the RawFunctor
record for lists, which
satisfies the functor laws:
rawFunctor : RawFunctor List
rawFunctor = record { fmap = fmap }
where
fmap : ∀ {A B} → (A → B) → List A → List B
fmap _ [] = []
fmap f (x ∷ xs) = f x ∷ fmap f xs
Again, we have to prove that List
and fmap
satisfy the functor
laws, but we're not going to do that yet.
However, we can include the functor laws in the definition of functors so that there's no way to define a functor which is not a functor:
record Functor (F : Set → Set) : Set₁ where
field
fmap : ∀ {A B} → (A → B) → F A → F B
fmap-id : ∀ {A} (fx : F A) → fmap id fx ≡ fx
fmap-∘ : ∀ {A B C} {f : A → B} {g : B → C}
(fx : F A) → fmap (g ∘ f) fx ≡ fmap g (fmap f fx)
Here, fmap-id
and fmap-∘
are fields just like fmap
. Given an
element fx
of F A
, fmap-id fx
and fmap-∘ fx
are proofs of the
first and second functor laws, respectively. Additionally, ≡
is just
a (dependent) type representing propositional (intensional) equality.
Thus, declaring a Functor
record is how we define a functor with a
strong specification in Agda.
For instance, here's the Functor
record for lists:
functor : Functor List
functor = record { fmap = fmap ; fmap-id = fmap-id ; fmap-∘ = fmap-∘ }
where
fmap-id : {A : Set} (xs : List A) → fmap id xs ≡ xs
fmap-id [] = refl
fmap-id (x ∷ xs) = cong (_∷_ x) (fmap-id xs)
fmap-∘ : ∀ {A B C} {f : A → B} {g : B → C}
(xs : List A) → fmap (g ∘ f) xs ≡ fmap g (fmap f xs)
fmap-∘ [] = refl
fmap-∘ {f = f} {g} (x ∷ xs) = cong (_∷_ (g (f x))) (fmap-∘ xs)
We prove both laws by induction and use the fact that propositional
equality is a reflexive and congruence relation. Even if the proofs'
details are not clear, what matters now is that this definition of the
Functor
record for lists satisfies our specification because it
contains actual proofs of the functor laws. Moreover, it's the only
way to define a type checking Functor
record for lists.
This is but one example of weak and strong specifications. Of course, strong specifications are more complex than weak ones, but they're also more explicit about the behavior of our functions and closer to the idea of having a certificate of the correctness of our programs.
What do you think?
(Based on an assignment for Dependently Typed Functional Languages, chapter 9 of Yves Bertot and Pierre Casterán's Interactive Theorem Proving and Program Development, some of Adam Chlipala's ideas on Certified Programming with Dependent Types, section 6 of Ana Bove and Peter Dybjer's Dependent Types at Work, and section 3 of the Typeclassopedia.)
You can get the code at https://github.com/jpvillaisaza/weak-strong/tree/master/weak-strong-functors/. ↩
Published on: Dec. 16, 2014
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