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Commit 28d7109a authored by Paul Fiterau Brostean's avatar Paul Fiterau Brostean
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Will need to optimize test algorithm

parent 795797a0
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z3gi/benchmark.py
View file @ 28d7109a
import functools
from abc import ABCMeta
from typing import Tuple, List, Dict
import collections
from encode.fa import DFAEncoder, MealyEncoder
from encode.iora import IORAEncoder
from encode.ra import RAEncoder
from encode.iora import IORAEncoder, IORAQREncoder
from encode.ra import RAEncoder, RAQREncoder
from learn import Learner
from learn.fa import FALearner
from learn.ra import RALearner
......@@ -29,11 +30,11 @@ class SutDesc(collections.namedtuple("SutDesc", 'sut_class type size')):
return str(self.type).replace("SUTType.","") + "_" + str(self.sut_class.__class__.__name__).replace("Class", "") + "(" + str(self.size) + ")"
TestDesc = collections.namedtuple("TestDesc", 'max_tests rand_length prop_reset')
class ExperimentStats(collections.namedtuple("CollectedStats", "states registers tests "
class ExperimentStats(collections.namedtuple("CollectedStats", "states minimal registers tests "
"inputs max_ltime learn_time")):
pass
class CollatedStats(collections.namedtuple("CollatedStats", "exp_succ states registers consistent avg_tests std_tests avg_inputs std_inputs max_ltime avg_ltime std_ltime")):
class CollatedStats(collections.namedtuple("CollatedStats", "exp_succ states registers minimal consistent avg_tests std_tests avg_inputs std_inputs max_ltime avg_ltime std_ltime")):
pass
def get_learner_setup(sut, sut_type:SUTType, size, test_desc:TestDesc):
......@@ -43,9 +44,13 @@ def get_learner_setup(sut, sut_type:SUTType, size, test_desc:TestDesc):
elif sut_type is SUTType.Mealy:
return (FALearner(MealyEncoder()), MealyRWalkFromState(*args))
elif sut_type is SUTType.RA:
return (RALearner(RAEncoder()), RARWalkFromState(*args))
ra_learner = RALearner(RAEncoder())
ra_learner.set_num_reg(size)
return (ra_learner, RARWalkFromState(*args))
elif sut_type is SUTType.IORA:
return (RALearner(IORAEncoder()), IORARWalkFromState(*args))
ra_learner = RALearner(IORAEncoder())
ra_learner.set_num_reg(size)
return (ra_learner, IORARWalkFromState(*args))
raise Exception("Invalid setup")
class Benchmark:
......@@ -68,10 +73,10 @@ class Benchmark:
self.learn_setup[sut_type] = (sut_learner, sut_tester)
return self
def _run_benchmark(self, sut_class:ScalableSUTClass, sut_type:SUTType, test_desc:TestDesc, tout:int, max_size:int, use_coloring:bool) \
-> List[Tuple[SutDesc, ExperimentStats]]:
def _run_benchmark(self, sut_class:ScalableSUTClass, sut_type:SUTType, test_desc:TestDesc, tout:int,\
min_size:int, max_size:int, use_coloring:bool) -> List[Tuple[SutDesc, ExperimentStats]]:
results = []
size = 1
size = min_size
while True:
if max_size is not None and size > max_size:
break
......@@ -101,25 +106,27 @@ class Benchmark:
if model is None:
break
else:
imp_stats = self._collect_stats(model, statistics)
sut_desc = SutDesc(sut_class, sut_type, size)
imp_stats = self._collect_stats(sut_desc, model, statistics)
results.append( (sut_desc, imp_stats))
size += 1
return results
def _collect_stats(self, model:Automaton, statistics:Statistics) -> ExperimentStats:
def _collect_stats(self, sut_desc:SutDesc, model:Automaton, statistics:Statistics) -> ExperimentStats:
states = len(model.states())
registers = len(model.registers()) if isinstance(model, RegisterMachine) else None
exp_size = sut_desc.sut_class.num_states(sut_desc.type, sut_desc.size)
minimal = None if exp_size is None else exp_size == len(model.states())
learn_tests = statistics.resets
learn_inputs = statistics.inputs
learn_time = sum(statistics.learning_times)
max_ltime = max(statistics.learning_times)
return ExperimentStats(states=states, registers=registers, tests=learn_tests, inputs=learn_inputs, max_ltime=max_ltime, learn_time=learn_time)
return ExperimentStats(states=states, minimal=minimal, registers=registers, tests=learn_tests, inputs=learn_inputs, max_ltime=max_ltime, learn_time=learn_time)
def run_benchmarks(self, test_desc:TestDesc, timeout:int, max_size:int=None, use_coloring:bool=False) -> List[Tuple[SutDesc, ExperimentStats]]:
def run_benchmarks(self, test_desc:TestDesc, timeout:int, min_size:int=1, max_size:int=1, use_coloring:bool=False) -> List[Tuple[SutDesc, ExperimentStats]]:
results = []
for sut_class, sut_type in self.suts:
res = self._run_benchmark(sut_class, sut_type, test_desc, timeout, max_size, use_coloring)
res = self._run_benchmark(sut_class, sut_type, test_desc, timeout, min_size, max_size, use_coloring)
results.extend(res)
return results
......@@ -134,6 +141,10 @@ def collate_stats(sut_desc: SutDesc, exp_stats:List[ExperimentStats]):
avg_reg = median(regs)
else:
avg_reg = None
if sut_desc.sut_class.num_states(sut_desc.type, sut_desc.size) is None:
minimal = None
else:
minimal = functools.reduce(lambda x,y: x & y, [e.minimal for e in exp_stats])
consistent = len(set(states)) == 1 and \
(not sut_desc.type.has_registers() or len(set(regs)) == 1)
exp_succ = len(exp_stats)
......@@ -145,6 +156,7 @@ def collate_stats(sut_desc: SutDesc, exp_stats:List[ExperimentStats]):
exp_succ=exp_succ,
states=avg_states,
registers=avg_reg,
minimal=minimal,
consistent=consistent,
avg_tests=median(ltests),
std_tests=0 if len(ltests) == 1 else stdev(ltests),
......@@ -177,14 +189,16 @@ b = Benchmark()
#b.add_setup(SUTType.IORA, RALearner(IORAEncoder()), IORARWalkFromState)
# add the sut classes we want to benchmark
#b.add_sut(FIFOSetClass())
b.add_sut(FIFOSetClass(), SUTType.DFA)
b.add_sut(LoginClass(), SUTType.DFA)
#b.add_sut(StackClass())
b.add_sut(FIFOSetClass(), SUTType.Mealy)
b.add_sut(LoginClass(), SUTType.Mealy)
#b.add_sut(StackClass(), SUTType.DFA)
#b.add_sut(FIFOSetClass(), SUTType.DFA)
#b.add_sut(FIFOSetClass(), SUTType.IORA)
#b.add_sut(LoginClass(), SUTType.IORA)
#b.add_sut(FIFOSetClass(), SUTType.RA)
#b.add_sut(LoginClass(), SUTType.Mealy)
#b.add_sut(StackClass(), SUTType.IORA)
# create a test description
......@@ -194,7 +208,10 @@ t_desc = TestDesc(max_tests=10000, prop_reset=0.2, rand_length=3)
timeout = 10000
# how many times each experiment should be run
num_exp = 2
num_exp = 5
# the start size
min_size = 1
# up to what systems of what size do we want to run experiments (set to None if size is ignored as a stop condition)
max_size = None
......@@ -205,7 +222,7 @@ use_coloring = False
# run the benchmark and collect results
results = []
for i in range(0, num_exp):
results += b.run_benchmarks(t_desc, timeout, max_size, use_coloring)
results += b.run_benchmarks(t_desc, timeout, min_size, max_size, use_coloring)
print("============================")
print_results(results)
sut_dict = dict()
......
z3gi/console.py
View file @ 28d7109a
......@@ -8,7 +8,7 @@ import model.fa
from encode.fa import MealyEncoder, DFAEncoder
from encode.iora import IORAEncoder
from encode.ra import RAEncoder
from encode.ra import RAEncoder, RAQREncoder
from learn.fa import FALearner
from learn.ra import RALearner
from test import AcceptorTest, IORATest, MealyTest
......@@ -20,7 +20,7 @@ from test.rwalk import DFARWalkFromState
"""some configuration mappings"""
aut2learner={
model.ra.RegisterAutomaton:RALearner(RAEncoder()),
model.ra.RegisterAutomaton:RALearner(RAQREncoder()),
model.ra.IORegisterAutomaton:RALearner(IORAEncoder()),
model.fa.MealyMachine:FALearner(MealyEncoder()),
model.fa.DFA:FALearner(DFAEncoder())
......@@ -73,7 +73,7 @@ if __name__ == '__main__':
parser.add_argument('-y', '--yannakakis', action='store_true', help='use yannakakis instead of rwalkfromstate '
'(only supports Mealy Machines)')
print(sut.scalable_sut_classes())
args = parser.parse_args()
formalism = args.aut
formalisms = model.defined_formalisms()
......
z3gi/define/fa.py
View file @ 28d7109a
......@@ -50,9 +50,6 @@ class Mapper(object):
self._elements = dict()
def element(self, name):
#if name not in self._elements:
# self._elements[name] = z3.Const("n"+str(name), self.Element)
#return self._elements[name]
return z3.Const("n"+str(name), self.Element)
......
z3gi/define/ra.py
View file @ 28d7109a
import z3
from model.ra import *
from define import dt_enum, Automaton
from define import dt_enum, Automaton, declsort_enum
class RegisterMachine(Automaton):
......@@ -10,6 +10,9 @@ class RegisterMachine(Automaton):
['register{0}'.format(i) for i in range(num_registers)] + ['fresh'])
self.param_size = param_size
def real_reg(self):
return self.registers[:-1]
class RegisterAutomaton(RegisterMachine):
def __init__(self, labels, param_size, num_locations, num_registers):
......@@ -24,6 +27,7 @@ class RegisterAutomaton(RegisterMachine):
self.update = z3.Function('update', self.Location, self.Label, self.Register)
def export(self, model : z3.ModelRef) -> RegisterAutomaton:
print(model)
builder = RegisterAutomatonBuilder(self)
ra = builder.build_ra(model)
return ra
......@@ -165,9 +169,9 @@ class IORegisterAutomatonBuilder(object):
self.ra.transition(self.ra.start, z3label, self.ra.fresh) != self.ra.sink))
]
z3output_labels = [z3label for z3label in self.ra.labels.values() if z3label not in z3input_labels]
print("Input labels ", z3input_labels, "; Output labels", z3output_labels,
"Input states ", z3input_states, "; Output and sink states",
[z3state for z3state in self.ra.locations if z3state not in z3input_states])
#print("Input labels ", z3input_labels, "; Output labels", z3output_labels,
# "Input states ", z3input_states, "; Output and sink states",
# [z3state for z3state in self.ra.locations if z3state not in z3input_states])
for z3state in z3input_states:
self._add_state(translator, mut_ra, z3state)
......@@ -229,7 +233,7 @@ class IORegisterAutomatonBuilder(object):
z3end_state = model.eval(self.ra.transition(z3out_state, z3output_label, z3output_guard))
z3output_update = model.eval(self.ra.update(z3out_state, z3output_label))
output_mapping = None if self.ra.param_size[translator.z3_to_label(z3output_label)] == 0 \
else translator.z3_to_mapping(z3output_guard)
else translator.z3_to_mapping(z3output_guard)
transition = IORATransition(translator.z3_to_state(z3start_state),
translator.z3_to_label(z3label),
......
z3gi/encode/fa.py
View file @ 28d7109a
......@@ -2,6 +2,7 @@ import itertools
import z3
from define import dt_enum
from define.fa import DFA, MealyMachine, Mapper
from encode import Encoder
from utils import Tree
......@@ -35,7 +36,8 @@ class DFAEncoder(Encoder):
for label in dfa.labels.values()]),
z3.Distinct(list(dfa.labels.values())),
z3.Distinct(list(dfa.states)),
z3.Distinct([mapper.element(node.id) for node in self.cache])
z3.Distinct([mapper.element(node.id) for node in self.cache]),
# z3.And([z3.Or([mapper.map(mapper.element(node.id)) == q for node in self.cache]) for q in dfa.states])
]
def node_constraints(self, dfa, mapper):
......@@ -89,9 +91,9 @@ class MealyEncoder(Encoder):
z3.Distinct(list(mm.inputs.values())),
z3.Distinct(list(mm.outputs.values())),
z3.Distinct(list(mm.states)),
z3.Distinct([mapper.element(node.id) for node in self.cache])
z3.Distinct([mapper.element(node.id) for node in self.cache]),
# z3.And([z3.Or([mapper.map(mapper.element(node.id)) == q for node in self.cache]) for q in mm.states])
]
#return []
def node_constraints(self, mm, mapper):
constraints = []
......
z3gi/encode/iora.py
View file @ 28d7109a
......@@ -249,7 +249,6 @@ class IORAEncoder(Encoder):
)
for l in self.output_labels:
if self.param_size[l] == 0:
print("Added for ", l)
axioms.append(
z3.ForAll(
[q,r],
......@@ -385,3 +384,362 @@ class IORAEncoder(Encoder):
constraints.append(z3.Distinct(list(values)))
return constraints
class IORAQREncoder(Encoder):
def __init__(self):
self.tree = Tree(itertools.count(0))
self.values = set()
self.input_labels = set()
self.output_labels = set()
self.param_size = dict()
def add(self, trace):
seq = list(itertools.chain(*map(iter, trace)))
_ = self.tree[determinize(seq)]
self.values.update([action.value for action in seq])
self.input_labels.update([action.label for action in [i for (i, _) in trace]])
self.output_labels.update([action.label for action in [o for (_, o) in trace]])
for action in seq:
if action.label not in self.param_size:
self.param_size[action.label] = action.param_size()
else:
if self.param_size[action.label] != action.param_size():
raise Exception("It is not allowed to have actions with different param sizes."
"Problem action: "+str(action))
def build(self, num_locations, num_registers):
ra = IORegisterAutomaton(list(self.input_labels), list(self.output_labels), self.param_size,
num_locations, num_registers)
mapper = Mapper(ra)
constraints = []
constraints.extend(self.axioms(ra, mapper))
constraints.extend(self.node_constraints(ra, mapper))
constraints.extend(self.transition_constraints(ra, mapper))
return ra, constraints
def axioms(self, ra: IORegisterAutomaton, mapper: Mapper):
#l, lp = z3.Consts('l lp', ra.Label)
#q, qp = z3.Consts('q qp', ra.Location)
#r, rp = z3.Consts('r rp', ra.Register)
norm_loc = set(ra.locations).difference(set([ra.sink]))
axioms = [
# In the start state of the mapper,
# all registers contain an uninitialized value.
# axiom not needed
z3.And([mapper.valuation(mapper.start, r) == mapper.init for r in ra.registers]),
# The fresh register is never used
z3.And([ra.used(q, ra.fresh) == False for q in norm_loc]),
# If a variable is used after a transition, it means it was either used before, or it was assigned
z3.And([
z3.Implies(
z3.And(
ra.used(ra.transition(q, l, rp), r) == True,
q != ra.sink,
r != ra.fresh
),
z3.Or(
ra.used(q, r) == True,
z3.And(
ra.update(q, l) == r,
rp == ra.fresh
),
)
) for rp in ra.registers for r in ra.registers for l in ra.labels.values() for q in ra.locations
]),
# If a variable is updated, then it should have been used.
z3.And([
z3.Implies(
z3.And(
r != ra.fresh,
ra.update(q, l) == r,
q != ra.sink
),
ra.used(ra.transition(q, l, ra.fresh), r) == True
) for r in ra.registers for l in ra.labels.values() for q in ra.locations
]
),
# Registers are not used in the start state
z3.And([
ra.used(ra.start, r) == False for r in ra.registers
])
]
l, lp = z3.Consts('l lp', ra.Label)
q, qp = z3.Consts('q qp', ra.Location)
r, rp = z3.Consts('r rp', ra.Register)
# # IO axioms
axioms.extend([
# The sink is labeled as an input location
ra.loctype(ra.sink) == True,
# Alternating input and output locations
z3.And(
[
z3.If(
ra.loctype(q) == True,
z3.And([z3.Or(
ra.loctype(ra.transition(q, l, r)) == False,
ra.transition(q, l, r) == ra.sink
) for l, r in itertools.product(ra.labels.values(), ra.registers)]),
z3.And([ra.loctype(ra.transition(q, l, r)) == True
for l, r in itertools.product(ra.labels.values(), ra.registers)])
) for q in ra.locations
]
),
# The sink is the only rejecting state
z3.And([
z3.If(
q == ra.sink,
ra.output(q) == False,
ra.output(q) == True
) for q in ra.locations]
),
# The start location is an input location
ra.loctype(ra.start) == True,
# more readable version
z3.ForAll(
[q],
z3.Implies(
z3.And(
q != ra.sink,
ra.loctype(q) == False,
),
z3.And(
z3.Exists(
[r, l],
ra.transition(q, l, r) != ra.sink,
),
z3.ForAll(
[r, l, rp, lp],
z3.Implies(
z3.And(
ra.transition(q, l, r) != ra.sink,
z3.Or(
rp != r,
lp != l,
)
),
ra.transition(q, lp, rp) == ra.sink
)
)
)
)
),
# The sink is a sink
z3.And([
ra.transition(ra.sink, l, r) == ra.sink for l, r in itertools.product(ra.labels.values(), ra.registers)]
),
# The sink doesn't update registers
z3.And([
ra.update(ra.sink, l) == ra.fresh for l in ra.labels.values()]
)
])
# From input locations, all output transitions go to sink.
axioms.extend([
z3.And([
z3.Implies(
ra.loctype(q) == True,
ra.transition(q, ra.labels[l], r) == ra.sink
)
for q, r in itertools.product(ra.locations, ra.registers)]
)
for l in self.output_labels])
# From output locations, all input transitions go to sink.
axioms.extend([
z3.And(
[
z3.Implies(
ra.loctype(q) == False,
ra.transition(q, ra.labels[l], r) == ra.sink
) for q,r in itertools.product(ra.locations, ra.registers)
]
)
for l in self.input_labels])
# Input enabled
axioms.extend([
z3.And(
[
z3.Implies(
z3.And(
ra.loctype(q) == True,
q != ra.sink
),
ra.transition(q, ra.labels[l], r) != ra.sink
) for q,r in itertools.product(ra.locations, ra.registers)]
)
for l in self.input_labels])
# parameter-less labels don't result in register updates
for l in itertools.chain(self.input_labels, self.output_labels):
if self.param_size[l] == 0:
axioms.append(
z3.And([
ra.update(q, ra.labels[l]) == ra.fresh for q in ra.locations]
)
)
# parameter-less input labels lead to the same transition for all registers
for l in self.input_labels:
if self.param_size[l] == 0:
axioms.append(
z3.And(
[
ra.transition(q, ra.labels[l], r) == ra.transition(q, ra.labels[l], ra.fresh)
for q,r in itertools.product(ra.locations, ra.registers)]
)
)
for l in self.output_labels:
if self.param_size[l] == 0:
axioms.append(
z3.And(
[
z3.Implies(
r != ra.fresh,
ra.transition(q, ra.labels[l], r) == ra.sink
) for q,r in itertools.product(ra.locations, ra.registers)]
)
)
return axioms
def node_constraints(self, ra, mapper):
constraints = []
for node in self.tree:
n = mapper.element(node.id)
constraints.append(mapper.map(n) != ra.sink)
return constraints
def transition_constraints(self, ra, mapper):
constraints = [ra.start == mapper.map(mapper.start), ra.sink != ra.start]
values = {mapper.init}
for node, (label, value), child in self.tree.transitions():
n = mapper.element(node.id)
l = ra.labels[label]
v = mapper.value(value) if value is not None else None
c = mapper.element(child.id)
# r, rp = z3.Consts('r rp', ra.Register)
if value is not None:
constraints.extend([
# If a non-fresh register has changed, it must have been updated
z3.And(
[
z3.Implies(
z3.And(
r != ra.fresh,
mapper.valuation(c, r) != mapper.valuation(n, r)
),
ra.update(mapper.map(n), l) == r
) for r in ra.registers]
),
# If a used, non-fresh register keeps its value, then it was not updated.
z3.And(
[
z3.Implies(
z3.And(
r != ra.fresh,
mapper.valuation(c, r) == mapper.valuation(n, r),
ra.used(mapper.map(n), r) == True
),
ra.update(mapper.map(n), l) != r
) for r in ra.registers]
),
])
if value is not None:
# If a non-fresh register is updated, and c and n are connected by fresh,
# then the register contains the value
# else the valuation is maintained.
constraints.append(z3.And(
[
z3.If(
z3.And(
r != ra.fresh,
ra.update(mapper.map(n), l) == r,
ra.transition(mapper.map(n), l, ra.fresh) == mapper.map(c)
),
mapper.valuation(c, r) == v,
mapper.valuation(c, r) == mapper.valuation(n, r)
) for r in ra.registers]
))
else:
constraints.append(
z3.And(
[
mapper.valuation(c, r) == mapper.valuation(n, r)
for r in ra.registers]
)
)
# Map to the right transition
path = [v for (_, v) in node.path() if v is not None]
if value in path:
if label in self.input_labels:
constraints.append(
z3.If(
z3.Or(
[
z3.And(
r != ra.fresh,
mapper.valuation(n, r) == v,
ra.used(mapper.map(n), r) == True
) for r in ra.registers]
),
z3.And(
[
z3.Implies(
z3.And(
r != ra.fresh,
mapper.valuation(n, r) == v,
ra.used(mapper.map(n), r) == True
),