# test-check

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nwjsmith 19:28:47

I have a function that takes as input a large set of uniformly distributed random numbers and a directed, acyclic graph. It returns a random topological ordering of the graph.

I’d like to hook this into test.check such that if I run test.check/quick-check twice with the same seed parameter then the same topological orderings will be generated.

nwjsmith 19:29:30

As I understand it, test.check’s generators and combinators are designed to keep me from having to see the underlying RNG

nwjsmith 19:30:57

and that’s worked really well until now, when I need a lot of uniformly distributed random numbers

nwjsmith 19:32:22

Is the test.check.generators/->Generator function considered part of the public API?

gfredericks 19:33:30

@nwjsmith so if you had a generator for uniformly distributed random numbers, would that solve it?

nwjsmith 19:36:22

@gfredericks yes, that would work beautifully

nwjsmith 19:37:31

Then I could do a (gen/vector (gen/rand-int max) num-elements) and fmap my random-topological-ordering over it

nwjsmith 19:55:10

(def gen-rand-double (gen/no-shrink (gen/->Generator (fn [rnd _size] (rose/pure (random/rand-double rnd)))))) ?

nwjsmith 19:58:05

Ah, there’s random/rand-long.

``` (def gen-rand-long (gen/no-shrink (gen/->Generator (fn [rnd _size] (rose/pure (random/rand-long rnd))))))

(gen/generate (gen/vector gen-rand-long) 10 100) ;; => [6200754525179398429 -3542882520031353397] (gen/generate (gen/vector gen-rand-long) 10 100) ;; => [6200754525179398429 -3542882520031353397] ```

nwjsmith 19:58:35

That seems to do the trick

nwjsmith 20:01:15

I’m not really sure what the effects of no-shrink are here, or what rose/pure is doing. I’ll have to watch you’re old talk again to brush up on the internals.

nwjsmith 20:02:17

Your talk from this week was fantastic by the way, really good guide to building custom generators.

gfredericks 20:04:01

(also, why do they have to be uniformly distributed?)

nwjsmith 20:06:07

the algorithm is on page 2. I’ve got to generate a large number of random swaps of a topological ordering.

nwjsmith 20:06:19

(the bottom of page 2)

nwjsmith 20:07:46

At first I tried gen/choose, but I ended up with really bad distributions on the generator.

gfredericks 20:09:02

Choose is uniform, unlike most other builtin generators

nwjsmith 20:09:35

Hrm. Maybe I just thought I’d end up with really bad distributions

nwjsmith 20:09:50

I’ll try choose again.

gfredericks 20:09:53

no-shrink is a noop in your code I believe

gfredericks 20:11:12

You need uniform ints or doubles?

gfredericks 20:11:32

What range?

nwjsmith 20:12:46

between 0 and 2n-1, where n is the number of nodes in the graph.

gfredericks 20:13:05

Oooh, yeah, choose is bad for large n

gfredericks 20:14:23

Foolproof is (gen/vector (gen/choose 0 1) n), then fmap the bits to a bigint

gfredericks 20:15:41

But even if the algorithm demands uniform, I'm curious if testing with a nonuniform generator would actually be bad

gfredericks 20:19:36

But either way, there's no builtin bigint generator yet, which makes it harder for you :(

nwjsmith 20:23:27

Putting it together with the approach you suggested should work though. I’m going to experiment a bit with all of the approaches above. Will report back.

nwjsmith 20:23:56

BTW do you have a strategy for testing generators? Specifically how they shrink, their distribution, performance etc.

nwjsmith 20:26:12

Experimenting at the REPL has been working for me, but now I have all of these useful graph-related generators and I’m hoping to open source them. Might have to come up with my own strategy.

gfredericks 20:41:37

No, that's an interesting idea. You can see examples in the tests of testing distribution & shrinking a bit, but it's all ad-hoc

gfredericks 20:41:48

Shrinking is particularly weird to test