Moving Beyond Types (2017)
hypothesis.works
hypothesis.works
(If you should first understand your problem before you write some code or if you should use code to help you understand the problem and then write more code is up to debate, of course. Typing is an invaluable tool of thought to help you understand your problem, yes, but it is just one more tool in the toolbox.)
The sad thing I noticed though is that, having hacked a mostly untyped Python code base during the last few days, making sure that all typing annotations you add to the code base are sound is a big PITA, to put it bluntly, and I would pretty much prefer to work with a statically typed language in this particular case. If your typing discipline is uncoupled from the ability to run code, people simply remove that obstacle from their way. It starts to be treated pretty much like tests and documentation: indispensable in theory, relegated to second plan in practice.
So my impression is that gradual typing almost got it right, but it may do a great disservice to overall code quality as well. I am now inclined to think that some kind of barbell strategy on typing will render better results: use both a completely untyped dynamic language such as Tcl, where everything is a string, and a static, strongly typed programming language (ideally a proof assistant with dependent types). You prototype with the former and move into production after translating your solution to the latter. If you ever need to push untyped code into production, it will be obvious to everyone involved and, since it is so decoupled, it cannot affect the quality of the statically typed codebase by any chance.
> Typing is an invaluable tool of thought to help you understand your problem, yes, but it is just one more tool in the toolbox
For me those two sentences contradict each others. Types force you to draw an outline of your solution. For any non trivial problem this will be a time saver even in the prototype phase.
This is gradual typing. The real problem with gradual typing is that you largely forgo the performance advantages of static typing, since you spend a lot of compute time translating data in and out of "dynamically-typed" (i.e. tagged/runtime dispatched) representations. This is especially obvious in stringly-typed languages ala Tcl, but applies elsewhere as well.
“Abrupt typing” would describe this approach more precisely, if there was such jargon. A program module has no types while it is a prototype, but it must be completely typed (which is just another term for “having its correctness proved with the best tools available at this day and age”) before it goes into production.
However, I don't think what the author is suggesting in its place better. The advantage of tying the test case generation to the type is that it's compositional. Lots of sub Arbitrary instances come together to generate the test case and in the long term I find this to be more maintainable. Whereas the author's approach discourages compositionally.
The real problem is that the types in vanilla Haskell are too loose, so there lots of inhabitants of a type that we are not interested in. The author mentions in passing, dependent-types might be a solution and that is exactly right. What is needed is to make the irrelevant terms illegal altogether. This is pretty achievable and is already somewhat used. For example, the program synthesis (proof search) mechanism of Idris 2[0] uses linear[1] dependent types[2] to heavily constrain which terms are possible candidates for a given type. The specificity of the type reduces the search space, hence increases the effectiveness of the proof search.
[0] https://github.com/edwinb/Idris2
[1] https://en.wikipedia.org/wiki/Substructural_type_system#Line...
https://ucsd-progsys.github.io/liquidhaskell-blog/
https://ucsd-progsys.github.io/liquidhaskell-tutorial/01-int...
The Python hypothesis package strikes me more like a dynamic version of Haskell/LiquidHaskell than Haskell/hedgehog.
> One of the big differences between Hypothesis and Haskell QuickCheck is how shrinking is handled.
> Specifically, the way shrinking is handled in Haskell QuickCheck is bad and the way it works in Hypothesis (and also in test.check and EQC) is good. If you’re implementing a property based testing system, you should use the good way. If you’re using a property based testing system and it doesn’t use the good way, you need to know about this failure mode.
> The big difference is whether shrinking is integrated into generation.
> Integrating shrinking into generation has two large benefits...
> The first is mostly important from a convenience point of view...
> But the second is <i>really</i> important, because the lack of it makes your test failures potentially extremely confusing.
We sure would! What's the issue?
Did you know it's one function call to change your NonEmptyList FiniteFloat into [Float]? It's called coerce. There's very little extra syntactic baggage.
And you know what, carrying data generators around is a bad idea. It forces you to think about how you want the random data to be generated, and not what the random data should look like.
If so.. why are we still using the notion that types = sets of values?
Most things in the real world don't have a fixed set of attributes, attributes can change over time and as software developers we need to model that. There is a reason why the largest package managers (with the most packages published) are for dynamically typed languages. It's simply more flexible; these packages can work with anything; no need to import type definitions that don't fit within your type system. They are unopinionated, they return only simple objects and therefore they are highly composable.
I find naming types to be a major pain. In complex projects, you often end up with multiple types that are almost the same but have a few attributes different. In reality these are the same type, it's just not supposed to have a single fixed schema.
For example, how would you model a tadpole which develops into a frog with a statically typed language? Do you need two different types for this? What about all the awkward intermediate states between tadpole and frog? How do you model those? Do you really need to create multiple different instances in the code to represent that one entity?
Dynamically typed languages can be much quicker and cheaper to write, making for more rapid business product cycles. The lack of type safety can largely be made up by adding to the already necessary unit tests.
Isn't that how the YouTube startup out-manoeuvred Google's much larger dev team working with static types?
Code isn't just written, though. It's read, modified, and maintained. Sure, you can write it faster. But are you building that program for this week, or for the next two decades?
(Yes, I know, you can be in a situation where writing it faster now is life-or-death for the company. Most of us won't spend most of our careers in that situation, though.)
Only it very much matters...
And to track it you have an ad-hoc, incomplete, custom, buggy, implementation of type checking in your head -- as opposed to in the program, and statically checked.
>Maybe it's a new kind of type that you could not have imagined when you first wrote the code, but if it supports the necessary method, it's going to work and you don't need to change or recompile your code to account for it.
You obviously need to change your code to have objects of that "new kind of type" (even if they are dynamic and untyped). Where would the new objects come from without changing the code in some place, the ether?
Supporting the necessary method is a very poor check for "it's going to work".
A "bomb" and a "lock" object might both have an "activate" method. You wouldn't want to call the former when you meant the latter.
You're still doing the same checks, only know you do only half of them, in your head, and whatever goes...
let frob something = something#run ...
and it will enforce the fact that whatever is passed to frob, it must have a method named "run", and it must have the requisite number of arguments and their types.The types are still there, though. They're just types like, "some object with method "run" with ...".
If the state is modeled in numeric terms then you can represent the intermediate state as a range of integers. You need to do this whether or not your program is typed.
Without types you're allowing the program to fall into a state that it doesn't know how to handle. This works because the program doesn't try to handle these intermediate invalid states. What happens when it eventually does try to handle that state? That's when bugs happen.
Static type systems are a brute force way to eliminate a certain class of bug. A dynamic language appears simpler because it doesn't even attempt to capture those errors. It's like saying your payment validation is "simpler" because it doesn't do CCV checks.
// Tadpole state, can swim but cannot walk.
return {
color: 'black',
size: 10,
speed: 5,
direction: 3.14,
swim: function () {
// ...
}
};
}function allowFrogToWalk(frog) {
frog.walk = function () {
// ...
};
}function allowFrogToJump(frog) {
frog.jump = function () {
// ...
};
}function makeFrogToxic(frog) {
frog.intoxicate = function (animal) {
// ...
};
}let frog = createFrog();
// ...
// Then when the frog reaches a certain size: allowFrogToWalk(frog);
// After the frog spends a certain amount of time on land.
allowFrogToJump(frog);
// If frog eats a certain toxic plant.
makeFrogToxic(frog);
// ...
// Let's say our game has many different kinds of animals and we manage them
// from a single place in the code (and they can interact with each other).
// When an animal tries to go on land:
if (animal.walk) {
// ...
}// If animal enounters an obstacle:
if (animal.jump) {
// ...
}// When an animal tries to go on water:
if (animal.swim) {
// ...
}// When two animals touch each other
if (touches(animalA, animalB)) {
if (animalA.intoxicate) {
animalA.intoxicate(animalB);
}
// ...
}Doing this with a statically typed language would add a lot of unnecessary complexity. You'd need to create a lot of types/interfaces:
- Animal (which has all the generic properties of an animal)
- WaterAnimal (which inherits from Animal and can swim but not walk)
- LandAnimal (which inherits from Animal and can walk but not swim)
- Tadpole (inherits from WaterAnimal)
- ToxicTadpole (inherits from tadpole but adds intoxicate method)
- YoungFrog (inherits from WaterAnimal and LandAnimal and is constructed from a Tadpole instance, cannot jump)
- ToxicYoungFrog (inherits from YoungFrog but adds intoxicate method)
- MatureFrog (inherits from WaterAnimal and LandAnimal and is constructed from a YoungFrog instance, has the ability to Jump)
- ToxicMatureFrog (inherits from MatureFrog but adds intoxicate method)
As you add more complex functionality, you either have to support more permutations of different types or you have come up with some kind of complex decorator pattern which subverts the whole purpose of having static types to begin with.
I also have to notice that this is not a very typical problem. I don't seem to remember that I have ever had to model something like this in a programming language.
"Don't do that then"
This kind of problem with using heavily inheritance-based systems for behavior can be addressed by using entity component systems. Often found in game development. The general guidance these days is to use "is-a" relationships less and "has-a" relationships more.
The point that I'm trying to make is that true compile-time type safety is not possible in my example without some kind of rigid complex inheritance structure that is predefined at compile time.
type walker = WalkFn of (animal -> unit)
and jumper = JumpFn of (animal -> unit)
and swimmer = SwimFn of (animal -> unit)
and intoxicator = ToxicFn of (animal -> animal -> unit)
and animal = Animal of animal_data
and animal_data = {
name: string;
color: string;
mutable walk: walker option;
mutable jump: jumper option;
mutable swim: swimmer option;
mutable intoxicate: intoxicator option
}
let can_walk = function
| Animal { walk = Some _ } -> true
| _ -> false
let walk animal =
match animal with
| Animal { walk = Some (WalkFn walk_fn) } -> walk_fn animal
| _ -> invalid_arg "this animal can't walk"
let default_walker = function
| Animal { name; color } -> Printf.printf "the %s %s walks\n" color name
let allow_to_walk (Animal data) =
data.walk <- Some (WalkFn default_walker)
(* --- more of the same for the other actions --- *)
let can_jump = function
| Animal { jump = Some _ } -> true
| _ -> false
let jump animal =
match animal with
| Animal { jump = Some (JumpFn jump_fn) } -> jump_fn animal
| _ -> invalid_arg "this animal can't jump"
let default_jumper = function
| Animal { name; color } -> Printf.printf "the %s %s jumps\n" color name
let allow_to_jump (Animal data) =
data.jump <- Some (JumpFn default_jumper)
let can_swim = function
| Animal { swim = Some _ } -> true
| _ -> false
let swim animal =
match animal with
| Animal { swim = Some (SwimFn swim_fn) } -> swim_fn animal
| _ -> invalid_arg "this animal can't swim"
let default_swimmer = function
| Animal { name; color } -> Printf.printf "the %s %s swims\n" color name
let allow_to_swim (Animal data) =
data.swim <- Some (SwimFn default_swimmer)
let can_intoxicate = function
| Animal { intoxicate = Some _ } -> true
| _ -> false
let intoxicate animal victim =
match animal with
| Animal { intoxicate = Some (ToxicFn toxic_fn) } -> toxic_fn animal victim
| _ -> invalid_arg "this animal can't intoxicate"
let default_intoxicator animal victim =
match animal, victim with
| Animal attacker, Animal victim ->
Printf.printf "the %s %s poisons the %s %s\n"
attacker.color attacker.name victim.color victim.name
let allow_to_intoxicate (Animal data) =
data.intoxicate <- Some (ToxicFn default_intoxicator)
(* --- end of actions --- *)
let create_frog () =
Animal {
name = "frog";
color = "black";
walk = None;
jump = None;
swim = None;
intoxicate = None
}
let create_cow () =
Animal {
name = "cow";
color = "green";
walk = None;
jump = None;
swim = None;
intoxicate = None
}
let story () =
let frog = create_frog () in
Printf.printf "newborn frog, can it walk? %s\n"
(if can_walk frog then "yes" else "no");
allow_to_walk frog;
Printf.printf "and now, can it walk? %s\n"
(if can_walk frog then "yes" else "no");
walk frog;
allow_to_swim frog;
swim frog;
allow_to_jump frog;
jump frog;
allow_to_intoxicate frog;
intoxicate frog (create_cow ());
Printf.printf "the end\n"
(* now run all this *)
let _ = story ()
And here's what it looks like when it's run: $ ocaml frog_story.ml
newborn frog, can it walk? no
and now, can it walk? yes
the black frog walks
the black frog swims
the black frog jumps
the black frog poisons the green cow
the end
It's true that the animal type must have types and fields for all possible actions an animal might ever take. It would be possible to compartmentalize things a bit more, maybe. But for a quick prototype this should work quite well, I think. let create_frog () = object
method color = "black"
method size = 10
method speed = 5
method direction = 3.14
method swim = print_endline "Frog swimming"
end
let allow_frog_to_walk frog = object
method walk = print_endline "Frog walking"
method super = frog
end
let allow_frog_to_jump frog = object
method jump = print_endline "Frog jumping"
method super = frog
end
let make_frog_toxic frog = object
method intoxicate animal = print_endline ("Intoxicate" ^ animal)
method super = frog
end
let frog = create_frog ()
let frog = allow_frog_to_walk frog
let frog = allow_frog_to_jump frog
let frog = make_frog_toxic frogWell, no. It is more like a ABS once you switched from a bike to a car that can go way more than 100kph. In theory, you can take all corners without, but it will be dangerous, you have to do more, and you cannot go quite as fast. And while everyone thinks they understand how it works, its inner mechanism is only understood by specialists.
Also, note that the article isn't implying what you are: "Where stronger types are naturally present in the program, taking advantage of them to get better testing is certainly worth doing..."
Early in my career, dynamically typed languages allowed me to push hard and fast and learn a lot in a short space of time via rapid prototyping.
If you don't care about error handling then you can wrangle hard with a dynamic language and it won't catch up with you for a long time. But eventually...
Now? I generally write even Python scripts over a few tens of lines with mypy running, because otherwise it becomes necessary to adopt some form of Hungarian notation anyway, and then you're just doing typing badly.
For the most part static typing prevents you from doing things you know you shouldn't anyway. Like tacking arbitrary attributes onto objects that get passed around.
APIs change indeed; and you want to know whether a type has changed, not simply be surprised.
The flip side here is that ALL of the core functions work on ALL of the core data types, so no matter what type of data you're working on (sets, maps, lists/stacks, vectors, even strings) you run in to the same handful of data manipulation functions. All of the persistent data structures are immutable, memory-efficient, and allow for easy value-to-value comparisons. The Clojure data structures can also be applied as functions themselves so e.g. a map is both a data structure and a mapping function from one set of values to another. Everything fits neatly together like Lego bricks, you NEVER write any adapter code in Clojure.
You do sometimes miss type information, especially when working with devs that aren't consistent in function parameter naming, but the trade-off is that you get a remarkably succinct and flexible language that really seems purpose-built to avoid the Lisp curse.
Maybe you can give us an example where this is an issue?
LOL, what?
If anything, it's the opposite.
Dynamic typing is the lesser easy-to-start with machine (say, a toy bike), and as the needs grow (for both safety, performance, and guarantees for working with larger codebases/teams) you add types...
>Most things in the real world don't have a fixed set of attributes, attributes can change over time and as software developers we need to model that.
Which is irrelevant. Our programs (functions, custom types, etc) have a fixed set of attributes at any given time -- as the code must know about them to use them in the first place.
For things that we model and don't have fixed attributes, we can and do build ADTs and data structures to accommodate them.