# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis-python
#
# Most of this work is copyright (C) 2013-2018 David R. MacIver
# (david@drmaciver.com), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at http://mozilla.org/MPL/2.0/.
#
# END HEADER
"""This module provides support for a stateful style of testing, where tests
attempt to find a sequence of operations that cause a breakage rather than just
a single value.
Notably, the set of steps available at any point may depend on the
execution to date.
"""
from __future__ import division, print_function, absolute_import
import inspect
import traceback
from unittest import TestCase
import attr
import hypothesis.internal.conjecture.utils as cu
from hypothesis.core import find
from hypothesis.errors import Flaky, NoSuchExample, InvalidDefinition, \
HypothesisException
from hypothesis.control import BuildContext
from hypothesis._settings import Verbosity
from hypothesis._settings import settings as Settings
from hypothesis.reporting import report, verbose_report, current_verbosity
from hypothesis.strategies import just, one_of, runner, tuples, \
fixed_dictionaries
from hypothesis.vendor.pretty import CUnicodeIO, RepresentationPrinter
from hypothesis.internal.reflection import proxies, nicerepr
from hypothesis.internal.conjecture.data import StopTest
from hypothesis.internal.conjecture.utils import integer_range, \
calc_label_from_name
from hypothesis.searchstrategy.strategies import SearchStrategy
STATE_MACHINE_RUN_LABEL = calc_label_from_name('another state machine step')
if False:
from typing import Any, Dict, List, Text # noqa
class TestCaseProperty(object): # pragma: no cover
def __get__(self, obj, typ=None):
if obj is not None:
typ = type(obj)
return typ._to_test_case()
def __set__(self, obj, value):
raise AttributeError(u'Cannot set TestCase')
def __delete__(self, obj):
raise AttributeError(u'Cannot delete TestCase')
def find_breaking_runner(state_machine_factory, settings=None):
def is_breaking_run(runner):
try:
runner.run(state_machine_factory())
return False
except HypothesisException:
raise
except Exception:
verbose_report(traceback.format_exc)
return True
if settings is None:
try:
settings = state_machine_factory.TestCase.settings
except AttributeError:
settings = Settings.default
search_strategy = StateMachineSearchStrategy(settings)
return find(
search_strategy,
is_breaking_run,
settings=settings,
database_key=state_machine_factory.__name__.encode('utf-8')
)
def run_state_machine_as_test(state_machine_factory, settings=None):
"""Run a state machine definition as a test, either silently doing nothing
or printing a minimal breaking program and raising an exception.
state_machine_factory is anything which returns an instance of
GenericStateMachine when called with no arguments - it can be a class or a
function. settings will be used to control the execution of the test.
"""
try:
breaker = find_breaking_runner(state_machine_factory, settings)
except NoSuchExample:
return
try:
with BuildContext(None, is_final=True):
breaker.run(state_machine_factory(), print_steps=True)
except StopTest:
pass
raise Flaky(
u'Run failed initially but succeeded on a second try'
)
[docs]class GenericStateMachine(object):
"""A GenericStateMachine is the basic entry point into Hypothesis's
approach to stateful testing.
The intent is for it to be subclassed to provide state machine descriptions
The way this is used is that Hypothesis will repeatedly execute something
that looks something like::
x = MyStatemachineSubclass()
x.check_invariants()
try:
for _ in range(n_steps):
x.execute_step(x.steps().example())
x.check_invariants()
finally:
x.teardown()
And if this ever produces an error it will shrink it down to a small
sequence of example choices demonstrating that.
"""
find_breaking_runner = None # type: classmethod
[docs] def steps(self):
"""Return a SearchStrategy instance the defines the available next
steps."""
raise NotImplementedError(u'%r.steps()' % (self,))
[docs] def execute_step(self, step):
"""Execute a step that has been previously drawn from self.steps()"""
raise NotImplementedError(u'%r.execute_step()' % (self,))
def print_start(self):
"""Called right at the start of printing.
By default does nothing.
"""
def print_end(self):
"""Called right at the end of printing.
By default does nothing.
"""
def print_step(self, step):
"""Print a step to the current reporter.
This is called right before a step is executed.
"""
self.step_count = getattr(self, u'step_count', 0) + 1
report(u'Step #%d: %s' % (self.step_count, nicerepr(step)))
[docs] def teardown(self):
"""Called after a run has finished executing to clean up any necessary
state.
Does nothing by default
"""
pass
[docs] def check_invariants(self):
"""Called after initializing and after executing each step."""
pass
_test_case_cache = {} # type: dict
TestCase = TestCaseProperty()
@classmethod
def _to_test_case(state_machine_class):
try:
return state_machine_class._test_case_cache[state_machine_class]
except KeyError:
pass
class StateMachineTestCase(TestCase):
settings = Settings()
# We define this outside of the class and assign it because you can't
# assign attributes to instance method values in Python 2
def runTest(self):
run_state_machine_as_test(state_machine_class)
runTest.is_hypothesis_test = True
StateMachineTestCase.runTest = runTest
base_name = state_machine_class.__name__
StateMachineTestCase.__name__ = str(
base_name + u'.TestCase'
)
StateMachineTestCase.__qualname__ = str(
getattr(state_machine_class, u'__qualname__', base_name) +
u'.TestCase'
)
state_machine_class._test_case_cache[state_machine_class] = (
StateMachineTestCase
)
return StateMachineTestCase
GenericStateMachine.find_breaking_runner = classmethod(find_breaking_runner)
class StateMachineRunner(object):
"""A StateMachineRunner is a description of how to run a state machine.
It contains values that it will use to shape the examples.
"""
def __init__(self, data, n_steps):
self.data = data
self.data.is_find = False
self.n_steps = n_steps
def run(self, state_machine, print_steps=None):
if print_steps is None:
print_steps = current_verbosity() >= Verbosity.debug
self.data.hypothesis_runner = state_machine
should_continue = cu.many(
self.data, min_size=1, max_size=self.n_steps,
average_size=self.n_steps,
)
try:
if print_steps:
state_machine.print_start()
state_machine.check_invariants()
while should_continue.more():
value = self.data.draw(state_machine.steps())
if print_steps:
state_machine.print_step(value)
state_machine.execute_step(value)
state_machine.check_invariants()
finally:
if print_steps:
state_machine.print_end()
state_machine.teardown()
class StateMachineSearchStrategy(SearchStrategy):
def __init__(self, settings=None):
self.program_size = (settings or Settings.default).stateful_step_count
def do_draw(self, data):
return StateMachineRunner(data, self.program_size)
@attr.s()
class Rule(object):
targets = attr.ib()
function = attr.ib()
arguments = attr.ib()
precondition = attr.ib()
self_strategy = runner()
class BundleReferenceStrategy(SearchStrategy):
def __init__(self, name):
self.name = name
def do_draw(self, data):
machine = data.draw(self_strategy)
bundle = machine.bundle(self.name)
if not bundle:
data.mark_invalid()
return bundle[integer_range(data, 0, len(bundle) - 1)]
class Bundle(SearchStrategy):
def __init__(self, name):
self.name = name
self.__reference_strategy = BundleReferenceStrategy(name)
def do_draw(self, data):
machine = data.draw(self_strategy)
reference = data.draw(self.__reference_strategy)
return machine.names_to_values[reference.name]
RULE_MARKER = u'hypothesis_stateful_rule'
PRECONDITION_MARKER = u'hypothesis_stateful_precondition'
INVARIANT_MARKER = u'hypothesis_stateful_invariant'
def rule(targets=(), target=None, **kwargs):
"""Decorator for RuleBasedStateMachine. Any name present in target or
targets will define where the end result of this function should go. If
both are empty then the end result will be discarded.
targets may either be a Bundle or the name of a Bundle.
kwargs then define the arguments that will be passed to the function
invocation. If their value is a Bundle then values that have previously
been produced for that bundle will be provided, if they are anything else
it will be turned into a strategy and values from that will be provided.
"""
if target is not None:
targets += (target,)
converted_targets = []
for t in targets:
while isinstance(t, Bundle):
t = t.name
converted_targets.append(t)
def accept(f):
existing_rule = getattr(f, RULE_MARKER, None)
if existing_rule is not None:
raise InvalidDefinition(
'A function cannot be used for two distinct rules. ',
Settings.default,
)
precondition = getattr(f, PRECONDITION_MARKER, None)
rule = Rule(targets=tuple(converted_targets), arguments=kwargs,
function=f, precondition=precondition)
@proxies(f)
def rule_wrapper(*args, **kwargs):
return f(*args, **kwargs)
setattr(rule_wrapper, RULE_MARKER, rule)
return rule_wrapper
return accept
@attr.s()
class VarReference(object):
name = attr.ib()
[docs]def precondition(precond):
"""Decorator to apply a precondition for rules in a RuleBasedStateMachine.
Specifies a precondition for a rule to be considered as a valid step in the
state machine. The given function will be called with the instance of
RuleBasedStateMachine and should return True or False. Usually it will need
to look at attributes on that instance.
For example::
class MyTestMachine(RuleBasedStateMachine):
state = 1
@precondition(lambda self: self.state != 0)
@rule(numerator=integers())
def divide_with(self, numerator):
self.state = numerator / self.state
This is better than using assume in your rule since more valid rules
should be able to be run.
"""
def decorator(f):
@proxies(f)
def precondition_wrapper(*args, **kwargs):
return f(*args, **kwargs)
rule = getattr(f, RULE_MARKER, None)
if rule is None:
setattr(precondition_wrapper, PRECONDITION_MARKER, precond)
else:
new_rule = Rule(targets=rule.targets, arguments=rule.arguments,
function=rule.function, precondition=precond)
setattr(precondition_wrapper, RULE_MARKER, new_rule)
invariant = getattr(f, INVARIANT_MARKER, None)
if invariant is not None:
new_invariant = Invariant(function=invariant.function,
precondition=precond)
setattr(precondition_wrapper, INVARIANT_MARKER, new_invariant)
return precondition_wrapper
return decorator
@attr.s()
class Invariant(object):
function = attr.ib()
precondition = attr.ib()
[docs]def invariant():
"""Decorator to apply an invariant for rules in a RuleBasedStateMachine.
The decorated function will be run after every rule and can raise an
exception to indicate failed invariants.
For example::
class MyTestMachine(RuleBasedStateMachine):
state = 1
@invariant()
def is_nonzero(self):
assert self.state != 0
"""
def accept(f):
existing_invariant = getattr(f, INVARIANT_MARKER, None)
if existing_invariant is not None:
raise InvalidDefinition(
'A function cannot be used for two distinct invariants.',
Settings.default,
)
precondition = getattr(f, PRECONDITION_MARKER, None)
rule = Invariant(function=f, precondition=precondition)
@proxies(f)
def invariant_wrapper(*args, **kwargs):
return f(*args, **kwargs)
setattr(invariant_wrapper, INVARIANT_MARKER, rule)
return invariant_wrapper
return accept
class RuleBasedStateMachine(GenericStateMachine):
"""A RuleBasedStateMachine gives you a more structured way to define state
machines.
The idea is that a state machine carries a bunch of types of data
divided into Bundles, and has a set of rules which may read data
from bundles (or just from normal strategies) and push data onto
bundles. At any given point a random applicable rule will be
executed.
"""
_rules_per_class = {} # type: Dict[type, List[classmethod]]
_invariants_per_class = {} # type: Dict[type, List[classmethod]]
_base_rules_per_class = {} # type: Dict[type, List[classmethod]]
def __init__(self):
if not self.rules():
raise InvalidDefinition(u'Type %s defines no rules' % (
type(self).__name__,
))
self.bundles = {} # type: Dict[Text, list]
self.name_counter = 1
self.names_to_values = {} # type: Dict[Text, Any]
self.__stream = CUnicodeIO()
self.__printer = RepresentationPrinter(self.__stream)
def __pretty(self, value):
if isinstance(value, VarReference):
return value.name
self.__stream.seek(0)
self.__stream.truncate(0)
self.__printer.output_width = 0
self.__printer.buffer_width = 0
self.__printer.buffer.clear()
self.__printer.pretty(value)
self.__printer.flush()
return self.__stream.getvalue()
def __repr__(self):
return u'%s(%s)' % (
type(self).__name__,
nicerepr(self.bundles),
)
def upcoming_name(self):
return u'v%d' % (self.name_counter,)
def new_name(self):
result = self.upcoming_name()
self.name_counter += 1
return result
def bundle(self, name):
return self.bundles.setdefault(name, [])
@classmethod
def rules(cls):
try:
return cls._rules_per_class[cls]
except KeyError:
pass
for k, v in inspect.getmembers(cls):
r = getattr(v, RULE_MARKER, None)
if r is not None:
cls.define_rule(
r.targets, r.function, r.arguments, r.precondition,
)
cls._rules_per_class[cls] = cls._base_rules_per_class.pop(cls, [])
return cls._rules_per_class[cls]
@classmethod
def invariants(cls):
try:
return cls._invariants_per_class[cls]
except KeyError:
pass
target = []
for k, v in inspect.getmembers(cls):
i = getattr(v, INVARIANT_MARKER, None)
if i is not None:
target.append(i)
cls._invariants_per_class[cls] = target
return cls._invariants_per_class[cls]
@classmethod
def define_rule(cls, targets, function, arguments, precondition=None):
converted_arguments = {}
for k, v in arguments.items():
converted_arguments[k] = v
if cls in cls._rules_per_class:
target = cls._rules_per_class[cls]
else:
target = cls._base_rules_per_class.setdefault(cls, [])
return target.append(
Rule(
targets, function, converted_arguments, precondition,
)
)
def steps(self):
strategies = []
for rule in self.rules():
converted_arguments = {}
valid = True
if rule.precondition and not rule.precondition(self):
continue
for k, v in sorted(rule.arguments.items()):
if isinstance(v, Bundle):
bundle = self.bundle(v.name)
if not bundle:
valid = False
break
v = BundleReferenceStrategy(v.name)
converted_arguments[k] = v
if valid:
strategies.append(tuples(
just(rule), fixed_dictionaries(converted_arguments)
))
if not strategies:
raise InvalidDefinition(
u'No progress can be made from state %r' % (self,)
)
return one_of(strategies)
def print_start(self):
report(u'state = %s()' % (self.__class__.__name__,))
def print_end(self):
report(u'state.teardown()')
def print_step(self, step):
rule, data = step
data_repr = {}
for k, v in data.items():
data_repr[k] = self.__pretty(v)
self.step_count = getattr(self, u'step_count', 0) + 1
report(u'%sstate.%s(%s)' % (
u'%s = ' % (self.upcoming_name(),) if rule.targets else u'',
rule.function.__name__,
u', '.join(u'%s=%s' % kv for kv in data_repr.items())
))
def execute_step(self, step):
rule, data = step
data = dict(data)
for k, v in list(data.items()):
if isinstance(v, VarReference):
data[k] = self.names_to_values[v.name]
result = rule.function(self, **data)
if rule.targets:
name = self.new_name()
self.names_to_values[name] = result
self.__printer.singleton_pprinters.setdefault(
id(result), lambda obj, p, cycle: p.text(name)
)
for target in rule.targets:
self.bundle(target).append(VarReference(name))
def check_invariants(self):
for invar in self.invariants():
if invar.precondition and not invar.precondition(self):
continue
invar.function(self)