Stateful tests¶

Note

With @given, your tests are still something that you mostly write yourself, with Hypothesis providing some data. With Hypothesis’s stateful testing, Hypothesis instead tries to generate not just data but entire tests. You specify a number of primitive actions that can be combined together, and then Hypothesis will try to find sequences of those actions that result in a failure.

You may not need stateful tests¶

The basic idea of stateful testing is to make Hypothesis choose actions as well as values for your test, and state machines are a great declarative way to do just that.

For simpler cases though, you might not need them at all - a standard test with @given might be enough, since you can use data() in branches or loops. In fact, that’s how the state machine explorer works internally. For more complex workloads though, where a higher level API comes into it’s own, keep reading!

Rule-based state machines¶

A state machine is very similar to a normal @given based test in that it takes values drawn from strategies and passes them to a user defined test function, which may use assertions to check the system’s behavior. The key difference is that where @given based tests must be independent, rules can be chained together - a single test run may involve multiple rule invocations, which may interact in various ways.

Rules can take normal strategies as arguments, but normal strategies, with the exception of runner() and data(), cannot take into account the current state of the machine. This is where bundles come in.

A rule can, in place of a normal strategy, take a Bundle. A hypothesis.stateful.Bundle is a named collection of generated values that can be reused by other operations in the test. They are populated with the results of rules, and may be used as arguments to rules, allowing data to flow from one rule to another, and rules to work on the results of previous computations or actions.

Specifically, a rule that specifies target=a_bundle will cause its return value to be added to that bundle. A rule that specifies an_argument=a_bundle as a strategy will draw a value from that bundle. A rule can also specify that an argument chooses a value from a bundle and removes that value by using consumes() as in an_argument=consumes(a_bundle).

Note

There is some overlap between what you can do with Bundles and what you can do with instance variables. Both represent state that rules can manipulate. If you do not need to draw values that depend on the machine’s state, you can simply use instance variables. If you do need to draw values that depend on the machine’s state, Bundles provide a fairly straightforward way to do this. If you need rules that draw values that depend on the machine’s state in some more complicated way, you will have to abandon bundles. You can use runner() and .flatmap() to access the instance from a rule: the strategy runner().flatmap(lambda self: sampled_from(self.a_list)) will draw from the instance variable a_list. If you need something more complicated still, you can use data() to draw data from the instance (or anywhere else) based on logic in the rule.

The following rule based state machine example is a simplified version of a test for Hypothesis’s example database implementation. An example database maps keys to sets of values, and in this test we compare one implementation of it to a simplified in memory model of its behaviour, which just stores the same values in a Python dict. The test then runs operations against both the real database and the in-memory representation of it and looks for discrepancies in their behaviour.

import shutil
import tempfile
from collections import defaultdict

import hypothesis.strategies as st
from hypothesis.database import DirectoryBasedExampleDatabase
from hypothesis.stateful import Bundle, RuleBasedStateMachine, rule

class DatabaseComparison(RuleBasedStateMachine):
    def __init__(self):
        super().__init__()
        self.tempd = tempfile.mkdtemp()
        self.database = DirectoryBasedExampleDatabase(self.tempd)
        self.model = defaultdict(set)

    keys = Bundle("keys")
    values = Bundle("values")

    @rule(target=keys, k=st.binary())
    def add_key(self, k):
        return k

    @rule(target=values, v=st.binary())
    def add_value(self, v):
        return v

    @rule(k=keys, v=values)
    def save(self, k, v):
        self.model[k].add(v)
        self.database.save(k, v)

    @rule(k=keys, v=values)
    def delete(self, k, v):
        self.model[k].discard(v)
        self.database.delete(k, v)

    @rule(k=keys)
    def values_agree(self, k):
        assert set(self.database.fetch(k)) == self.model[k]

    def teardown(self):
        shutil.rmtree(self.tempd)

TestDBComparison = DatabaseComparison.TestCase

In this we declare two bundles - one for keys, and one for values. We have two trivial rules which just populate them with data (k and v), and three non-trivial rules: save saves a value under a key and delete removes a value from a key, in both cases also updating the model of what should be in the database. values_agree then checks that the contents of the database agrees with the model for a particular key.

Note

While this could have been simplified by not using bundles, generating keys and values directly in the save and delete rules, using bundles encourages Hypothesis to choose the same keys and values for multiple operations. The bundle operations establish a “universe” of keys and values that are used in the rules.

We can now integrate this into our test suite by getting a unittest TestCase from it:

TestTrees = DatabaseComparison.TestCase

# Or just run with pytest's unittest support
if __name__ == "__main__":
    unittest.main()

This test currently passes, but if we comment out the line where we call self.model[k].discard(v), we would see the following output when run under pytest:

AssertionError: assert set() == {b''}

------------ Hypothesis ------------

state = DatabaseComparison()
var1 = state.add_key(k=b'')
var2 = state.add_value(v=var1)
state.save(k=var1, v=var2)
state.delete(k=var1, v=var2)
state.values_agree(k=var1)
state.teardown()

Note how it’s printed out a very short program that will demonstrate the problem. The output from a rule based state machine should generally be pretty close to Python code - if you have custom repr implementations that don’t return valid Python then it might not be, but most of the time you should just be able to copy and paste the code into a test to reproduce it.

You can control the detailed behaviour with a settings object on the TestCase (this is a normal hypothesis settings object using the defaults at the time the TestCase class was first referenced). For example if you wanted to run fewer examples with larger programs you could change the settings to:

DatabaseComparison.TestCase.settings = settings(
    max_examples=50, stateful_step_count=100
)

Which doubles the number of steps each program runs and halves the number of test cases that will be run.

Rules¶

As said earlier, rules are the most common feature used in RuleBasedStateMachine. They are defined by applying the rule() decorator on a function. Note that RuleBasedStateMachine must have at least one rule defined and that a single function cannot be used to define multiple rules (this is to avoid having multiple rules doing the same things). Due to the stateful execution method, rules generally cannot take arguments from other sources such as fixtures or pytest.mark.parametrize - consider providing them via a strategy such as sampled_from() instead.

Initializes¶

Initializes are a special case of rules, which are guaranteed to be run exactly once before any normal rule is called. Note if multiple initialize rules are defined, they will all be called but in any order, and that order will vary from run to run.

Initializes are typically useful to populate bundles:

import hypothesis.strategies as st
from hypothesis.stateful import Bundle, RuleBasedStateMachine, initialize, rule

name_strategy = st.text(min_size=1).filter(lambda x: "/" not in x)

class NumberModifier(RuleBasedStateMachine):
    folders = Bundle("folders")
    files = Bundle("files")

    @initialize(target=folders)
    def init_folders(self):
        return "/"

    @rule(target=folders, parent=folders, name=name_strategy)
    def create_folder(self, parent, name):
        return f"{parent}/{name}"

    @rule(target=files, parent=folders, name=name_strategy)
    def create_file(self, parent, name):
        return f"{parent}/{name}"

Initializes can also allow you to initialize the system under test in a way that depends on values chosen from a strategy. You could do this by putting an instance variable in the state machine that indicates whether the system under test has been initialized or not, and then using preconditions (below) to ensure that exactly one of the rules that initialize it get run before any rules that depend on it being initialized.

Preconditions¶

While it’s possible to use assume() in RuleBasedStateMachine rules, if you use it in only a few rules you can quickly run into a situation where few or none of your rules pass their assumptions. Thus, Hypothesis provides a precondition() decorator to avoid this problem. The precondition() decorator is used on rule-decorated functions, and must be given a function that returns True or False based on the RuleBasedStateMachine instance.

from hypothesis.stateful import RuleBasedStateMachine, precondition, rule

class NumberModifier(RuleBasedStateMachine):
    num = 0

    @rule()
    def add_one(self):
        self.num += 1

    @precondition(lambda self: self.num != 0)
    @rule()
    def divide_with_one(self):
        self.num = 1 / self.num

By using precondition() here instead of assume(), Hypothesis can filter the inapplicable rules before running them. This makes it much more likely that a useful sequence of steps will be generated.

Note that currently preconditions can’t access bundles; if you need to use preconditions, you should store relevant data on the instance instead.

Invariants¶

Often there are invariants that you want to ensure are met after every step in a process. It would be possible to add these as rules that are run, but they would be run zero or multiple times between other rules. Hypothesis provides a decorator that marks a function to be run after every step.

from hypothesis.stateful import RuleBasedStateMachine, invariant, rule

class NumberModifier(RuleBasedStateMachine):
    num = 0

    @rule()
    def add_two(self):
        self.num += 2
        if self.num > 50:
            self.num += 1

    @invariant()
    def is_even(self):
        assert self.num % 2 == 0

NumberTest = NumberModifier.TestCase

Invariants can also have precondition()s applied to them, in which case they will only be run if the precondition function returns true.

Note that currently invariants can’t access bundles; if you need to use invariants, you should store relevant data on the instance instead.

More fine grained control¶

If you want to bypass the TestCase infrastructure you can invoke these manually. The stateful module exposes the function run_state_machine_as_test, which takes an arbitrary function returning a RuleBasedStateMachine and an optional settings parameter and does the same as the class based runTest provided.