Source code for hypothesis.stateful

# coding=utf-8
# This file is part of Hypothesis, which may be found at
# Most of this work is copyright (C) 2013-2018 David R. MacIver
# (, 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

"""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 copy import copy
from unittest import TestCase

import attr

import hypothesis.internal.conjecture.utils as cu
from hypothesis.core import EXCEPTIONS_TO_FAIL, find
from hypothesis.errors import Flaky, NoSuchExample, InvalidArgument, \
    InvalidDefinition, HypothesisException
from hypothesis.control import BuildContext
from hypothesis._settings import Verbosity
from hypothesis._settings import settings as Settings
from hypothesis._settings import note_deprecation
from hypothesis.reporting import report, verbose_report, current_verbosity
from hypothesis.strategies import just, one_of, runner, tuples, \
from hypothesis.vendor.pretty import CUnicodeIO, RepresentationPrinter
from hypothesis.internal.compat import int_to_bytes, string_types
from hypothesis.internal.reflection import proxies, nicerepr
from import StopTest
from hypothesis.internal.conjecture.utils import integer_range, \
from hypothesis.searchstrategy.strategies import OneOfStrategy, \

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):
            return False
        except HypothesisException:
        except EXCEPTIONS_TO_FAIL:
            return True
    if settings is None:
            settings = state_machine_factory.TestCase.settings
        except AttributeError:
            settings = Settings.default

    search_strategy = StateMachineSearchStrategy(settings)

    return find(

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.
        breaker = find_breaking_runner(state_machine_factory, settings)
    except NoSuchExample:
        with BuildContext(None, is_final=True):
  , print_steps=True)
    except StopTest:
    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): = data = 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 = state_machine should_continue = cu.many(, 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 = 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() bundles = attr.ib(init=False) def __attrs_post_init__(self): arguments = {} bundles = [] for k, v in sorted(self.arguments.items()): assert not isinstance(v, BundleReferenceStrategy) if isinstance(v, Bundle): bundles.append(v) arguments[k] = BundleReferenceStrategy( else: arguments[k] = v self.bundles = tuple(bundles) self.arguments_strategy = fixed_dictionaries(arguments) self_strategy = runner() class BundleReferenceStrategy(SearchStrategy): def __init__(self, name): = name def do_draw(self, data): machine = data.draw(self_strategy) bundle = machine.bundle( if not bundle: data.mark_invalid() # Shrink towards the right rather than the left. This makes it easier # to delete data generated earlier, as when the error is towards the # end there can be a lot of hard to remove padding. return bundle[ integer_range(data, 0, len(bundle) - 1, center=len(bundle)) ] class Bundle(SearchStrategy): def __init__(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[] def _convert_targets(targets, target): """Single validator and convertor for target arguments.""" if target is not None: if targets: note_deprecation( 'Passing both targets=%r and target=%r is redundant, and ' 'will become an error in a future version of Hypothesis. ' 'Pass targets=%r instead.' % (targets, target, tuple(targets) + (target,)) ) targets = tuple(targets) + (target,) converted_targets = [] for t in targets: if isinstance(t, string_types): note_deprecation( 'Got %r as a target, but passing the name of a Bundle is ' 'deprecated - please pass the Bundle directly.' % (t,) ) elif not isinstance(t, Bundle): msg = 'Got invalid target %r of type %r, but all targets must ' \ 'be either a Bundle or the name of a Bundle.' if isinstance(t, OneOfStrategy): msg += ( '\nIt looks like you passed `one_of(a, b)` or `a | b` as ' 'a target. You should instead pass `targets=(a, b)` to ' 'add the return value of this rule to both the `a` and ' '`b` bundles, or define a rule for each target if it ' 'should be added to exactly one.' ) raise InvalidArgument(msg % (t, type(t))) while isinstance(t, Bundle): t = converted_targets.append(t) return tuple(converted_targets) RULE_MARKER = u'hypothesis_stateful_rule' INITIALIZE_RULE_MARKER = u'hypothesis_stateful_initialize_rule' PRECONDITION_MARKER = u'hypothesis_stateful_precondition' INVARIANT_MARKER = u'hypothesis_stateful_invariant'
[docs]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. ``target`` must be a Bundle, or if the result should go to multiple bundles you can pass a tuple of them as the ``targets`` argument. It is invalid to use both arguments for a single rule. If the result should go to exactly one of several bundles, define a separate rule for each case. 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. """ converted_targets = _convert_targets(targets, target) def accept(f): existing_rule = getattr(f, RULE_MARKER, None) existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None) if existing_rule is not None or existing_initialize_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=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
[docs]def initialize(targets=(), target=None, **kwargs): """Decorator for RuleBasedStateMachine. An initialize decorator behaves like a rule, but the decorated method is called at most once in a run. All initialize decorated methods will be called before any rule decorated methods, in an arbitrary order. """ converted_targets = _convert_targets(targets, target) def accept(f): existing_rule = getattr(f, RULE_MARKER, None) existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None) if existing_rule is not None or existing_initialize_rule is not None: raise InvalidDefinition( 'A function cannot be used for two distinct rules. ', Settings.default, ) precondition = getattr(f, PRECONDITION_MARKER, None) if precondition: raise InvalidDefinition( 'An initialization rule cannot have a precondition. ', Settings.default, ) rule = Rule(targets=converted_targets, arguments=kwargs, function=f, precondition=precondition) @proxies(f) def rule_wrapper(*args, **kwargs): return f(*args, **kwargs) setattr(rule_wrapper, INITIALIZE_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) existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None) if existing_initialize_rule is not None: raise InvalidDefinition( 'An initialization rule cannot have a precondition. ', Settings.default, ) 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
LOOP_LABEL = cu.calc_label_from_name('RuleStrategy loop iteration') class RuleStrategy(SearchStrategy): def __init__(self, machine): SearchStrategy.__init__(self) self.machine = machine self.rules = list(machine.rules()) # The order is a bit arbitrary. Primarily we're trying to group rules # that write to the same location together, and to put rules with no # target first as they have less effect on the structure. We order from # fewer to more arguments on grounds that it will plausibly need less # data. This probably won't work especially well and we could be # smarter about it, but it's better than just doing it in definition # order. self.rules.sort(key=lambda rule: ( sorted(rule.targets), len(rule.arguments), rule.function.__name__, )) def do_draw(self, data): # This strategy is slightly strange in its implementation. # We don't want the interpretation of the rule we draw to change based # on whether other rules satisfy their preconditions or have data in # their bundles. Therefore the index into the rule list needs to stay # stable. BUT we don't want to draw invalid rules. So what we do is we # draw an index. We *could* just loop until it's valid, but if most # rules are invalid then that could result in a very long loop. # So what we do is the following: # # 1. We first draw a rule unconditionally, and check if it's valid. # If it is, great. Nothing more to do, that's our rule. # 2. If it is invalid, we now calculate the list of valid rules and # draw from that list (if there are none, that's an error in the # definition of the machine and we complain to the user about it). # 3. Once we've drawn a valid rule, we write that back to the byte # stream. As a result, when shrinking runs the shrinker can delete # the initial failed draw + the draw that lead to us finding an # index into valid_rules, leaving just the written value of i. # When this is run, it will look as we got lucky and just happened # to pick a valid rule. # # Easy, right? n = len(self.rules) i = cu.integer_range(data, 0, n - 1) u, v = data.blocks[-1].bounds block_length = v - u rule = self.rules[i] if not self.is_valid(rule): valid_rules = [ j for j, r in enumerate(self.rules) if self.is_valid(r) ] if not valid_rules: raise InvalidDefinition( u'No progress can be made from state %r' % (self.machine,) ) i = valid_rules[cu.integer_range(data, 0, len(valid_rules) - 1)] data.write(int_to_bytes(i, block_length)) rule = self.rules[i] return (rule, data.draw(rule.arguments_strategy)) def is_valid(self, rule): if rule.precondition and not rule.precondition(self.machine): return False for b in rule.bundles: bundle = self.machine.bundle( if not bundle: return False return True
[docs]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]] _initializers_per_class = {} # type: Dict[type, List[classmethod]] _base_initializers_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) self._initialize_rules_to_run = copy(self.initialize_rules()) self.__rules_strategy = RuleStrategy(self) def __pretty(self, value): if isinstance(value, VarReference): return 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 initialize_rules(cls): try: return cls._initializers_per_class[cls] except KeyError: pass for k, v in inspect.getmembers(cls): r = getattr(v, INITIALIZE_RULE_MARKER, None) if r is not None: cls.define_initialize_rule( r.targets, r.function, r.arguments, r.precondition, ) cls._initializers_per_class[cls] = \ cls._base_initializers_per_class.pop(cls, []) return cls._initializers_per_class[cls] @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_initialize_rule( cls, targets, function, arguments, precondition=None): converted_arguments = {} for k, v in arguments.items(): converted_arguments[k] = v if cls in cls._initializers_per_class: target = cls._initializers_per_class[cls] else: target = cls._base_initializers_per_class.setdefault(cls, []) return target.append( Rule( targets, function, converted_arguments, precondition, ) ) @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): # Pick initialize rules first if self._initialize_rules_to_run: return one_of([ tuples(just(rule), fixed_dictionaries(rule.arguments)) for rule in self._initialize_rules_to_run ]) return self.__rules_strategy 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[] 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)) if self._initialize_rules_to_run: self._initialize_rules_to_run.remove(rule) def check_invariants(self): for invar in self.invariants(): if invar.precondition and not invar.precondition(self): continue invar.function(self)