Hypothesis for Django users¶
Hypothesis offers a number of features specific for Django testing, available
hypothesis[django] extra. This is tested
against each supported series with mainstream or extended support -
if you’re still getting security patches, you can test with Hypothesis.
Using it is quite straightforward: All you need to do is subclass
and you can use
@given as normal,
and the transactions will be per example
rather than per test function as they would be if you used
@given with a normal
django test suite (this is important because your test function will be called
multiple times and you don’t want them to interfere with each other). Test cases
on these classes that do not use
@given will be run as normal.
I strongly recommend not using
unless you really have to.
Because Hypothesis runs this in a loop the performance problems it normally has
are significantly exacerbated and your tests will be really slow.
If you are using
you may need to use
to avoid errors due to slow example generation.
In addition to the above, Hypothesis has some support for automatically deriving strategies for your model types, which you can then customize further.
Hypothesis creates saved models. This will run inside your testing transaction when using the test runner, but if you use the dev console this will leave debris in your database.
For example, using the trivial django project I have for testing:
>>> from hypothesis.extra.django.models import models >>> from toystore.models import Customer >>> c = models(Customer).example() >>> c <Customer: Customer object> >>> c.email 'firstname.lastname@example.org' >>> c.name '\U00109d3d\U000e07be\U000165f8\U0003fabf\U000c12cd\U000f1910\U00059f12\U000519b0\U0003fabf\U000f1910\U000423fb\U000423fb\U00059f12\U000e07be\U000c12cd\U000e07be\U000519b0\U000165f8\U0003fabf\U0007bc31' >>> c.age -873375803
Hypothesis has just created this with whatever the relevant type of data is.
Obviously the customer’s age is implausible, so lets fix that:
>>> from hypothesis.strategies import integers >>> c = models(Customer, age=integers(min_value=0, max_value=120)).example() >>> c <Customer: Customer object> >>> c.age 5
You can use this to override any fields you like. Sometimes this will be mandatory: If you have a non-nullable field of a type Hypothesis doesn’t know how to create (e.g. a foreign key) then the models function will error unless you explicitly pass a strategy to use there.
Foreign keys are not automatically derived. If they’re nullable they will default to always being null, otherwise you always have to specify them. e.g. suppose we had a Shop type with a foreign key to company, we would define a strategy for it as:
shop_strategy = models(Shop, company=models(Company))
Tips and tricks¶
Custom field types¶
If you have a custom Django field type you can register it with Hypothesis’s model deriving functionality by registering a default strategy for it:
>>> from toystore.models import CustomishField, Customish >>> models(Customish).example() hypothesis.errors.InvalidArgument: Missing arguments for mandatory field customish for model Customish >>> from hypothesis.extra.django.models import add_default_field_mapping >>> from hypothesis.strategies import just >>> add_default_field_mapping(CustomishField, just("hi")) >>> x = models(Customish).example() >>> x.customish 'hi'
Note that this mapping is on exact type. Subtypes will not inherit it.
Generating child models¶
For the moment there’s no explicit support in hypothesis-django for generating dependent models. i.e. a Company model will generate no Shops. However if you want to generate some dependent models as well, you can emulate this by using the flatmap function as follows:
from hypothesis.strategies import lists, just def generate_with_shops(company): return lists(models(Shop, company=just(company))).map(lambda _: company) company_with_shops_strategy = models(Company).flatmap(generate_with_shops)
Lets unpack what this is doing:
The way flatmap works is that we draw a value from the original strategy, then
apply a function to it which gives us a new strategy. We then draw a value from
that strategy. So in this case we’re first drawing a company, and then we’re
drawing a list of shops belonging to that company: The just strategy is a
strategy such that drawing it always produces the individual value, so
models(Shop, company=just(company)) is a strategy that generates a Shop belonging
to the original company.
So the following code would give us a list of shops all belonging to the same company:
models(Company).flatmap(lambda c: lists(models(Shop, company=just(c))))
The only difference from this and the above is that we want the company, not the shops. This is where the inner map comes in. We build the list of shops and then throw it away, instead returning the company we started for. This works because the models that Hypothesis generates are saved in the database, so we’re essentially running the inner strategy purely for the side effect of creating those children in the database.
Using default field values¶
Hypothesis ignores field defaults and always tries to generate values, even if
it doesn’t know how to. You can tell it to use the default value for a field
instead of generating one by passing
>>> from toystore.models import DefaultCustomish >>> models(DefaultCustomish).example() hypothesis.errors.InvalidArgument: Missing arguments for mandatory field customish for model DefaultCustomish >>> from hypothesis.extra.django.models import default_value >>> x = models(DefaultCustomish, customish=default_value).example() >>> x.customish 'b'