def test_constraint_context_sigma(self):
fsm = master_be_fsm()
_true = Node.from_ptr(parse_ltl_spec("TRUE"))
_true = bmcutils.make_nnf_boolean_wff(_true)
_truen= _true.to_node()
cond = Wff(parse_ltl_spec("G !(mouse = hover)"))\
.to_boolean_wff()\
.to_negation_normal_form()
off_1 = 0
off_2 = 2
length= 1
# sigma1
problem = diagnosability.generate_sat_problem([], (_truen, _truen), length, _true, cond.to_node(), _truen)
tm_cond = ltlspec.bounded_semantics_at_offset(fsm, cond.to_node(), length, off_1)
canonical_p = tests.canonical_cnf(problem)
canonical_f = tests.canonical_cnf(tm_cond)
self.assertTrue(all(clause in canonical_p for clause in canonical_f))
# sigma2
problem = diagnosability.generate_sat_problem([], (_truen, _truen), length, _true, _truen, cond.to_node())
tm_cond = ltlspec.bounded_semantics_at_offset(fsm, cond.to_node(), length, off_2)
canonical_p = tests.canonical_cnf(problem)
canonical_f = tests.canonical_cnf(tm_cond)
self.assertTrue(all(clause in canonical_p for clause in canonical_f))
def generate_sat_problem(observable_vars, formula_nodes, length, theta, sigma1, sigma2):
"""
Generates a SAT problem which is satisfiable iff the given `formula` is
*NOT* diagnosable for the loaded model for traces of length `length`.
:param observable_vars: the list of the boolean variables that are considered
visible in the scope of this diagnosability test
:param formula: the node (NuSMV ast representation) representing the formula
whose diagnosability is under verification
:param length: the maximum length of the generated traces.
:param theta: the initial condition placed on the initial belief state
(in the form of a :see:`pynusmv.node.Node`)
:param sigma1: the shape of the traces considered relevant for the first
member of the critical pair in the ongoing diagnosability test
(in the form of a :see:`pynusmv.node.Node`)
:param sigma2: the shape of the traces considered relevant for the second
member of the critical pair in the ongoing diagnosability test
(in the form of a :see:`pynusmv.node.Node`)
:return: a SAT problem which is satisfiable iff the given formula is not
diagnosable on the loaded model.
"""
fsm = master_be_fsm()
offset_1 = 0
offset_2 = length +1
problem = generate_path(offset_1, length) & generate_path(offset_2, length) \
& constraint_same_observations(
observable_vars, offset_1, offset_2, length)\
& constraint_context_theta_initial(theta, offset_1, offset_2) \
& bounded_semantics_at_offset(fsm, sigma1, length, offset_1) \
& bounded_semantics_at_offset(fsm, sigma2, length, offset_2) \
& constraint_eventually_critical_pair(
formula_nodes, offset_1, offset_2, length)
return problem
def test_releases(self):
with Configure(self, __file__, "/models/flipflops.smv"):
fsm = self.befsm
formula = self.nnf("(a V b)")
# bound 0
offset = 0
bound = 0
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 0
bound = 1
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
# VERIFIED manually, complains only about the CNF clauses literals and that's OK.
# self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# ---- other offset ----
# bound 2
offset = 2
bound = 1
# because of the way the loop condition is encoded !
ref_expr= ltlspec.bounded_semantics_without_loop_at_offset(fsm, formula, 0, bound, offset)\
| ( ltlspec.bounded_semantics_with_loop_at_offset(fsm, formula, 0, bound, 0, offset)
& bmcutils.loop_condition(self.enc, offset+bound, offset+0))
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
def generate_sat_problem(fsm, fml, length):
fsm = master_be_fsm()
offset = 0
wff = bmcutils.make_negated_nnf_boolean_wff(fml).to_node()
problem = generate_path(offset, length) \
& ltlspec.bounded_semantics_at_offset(fsm, wff, length, offset)
return problem
def generate_sat_problem(fsm, fml, length):
fsm = master_be_fsm()
offset = 0
wff = bmcutils.make_negated_nnf_boolean_wff(fml).to_node()
problem = generate_path(offset, length) \
& ltlspec.bounded_semantics_at_offset(fsm, wff, length, offset)
return problem
def test_until(self):
with Configure(self, __file__, "/models/flipflops.smv"):
fsm = self.befsm
formula = self.nnf("(a U !b)")
# bound 0
offset = 0
bound = 0
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 0
bound = 1
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# ---- other offset ----
# bound 0
offset = 2
bound = 0
cdr = Wff.decorate(ltlspec.cdr(formula)).to_be(fsm.encoding)
ref_expr= self.enc.shift_to_time(cdr, 0+offset)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 2
bound = 1
car = Wff.decorate(ltlspec.car(formula)).to_be(fsm.encoding)
cdr = Wff.decorate(ltlspec.cdr(formula)).to_be(fsm.encoding)
ref_expr= self.enc.shift_to_time(cdr, 0+offset) \
| (self.enc.shift_to_time(car, 0+offset) & self.enc.shift_to_time(cdr, 1+offset))
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
def test_globally(self):
with Configure(self, __file__, "/models/flipflops.smv"):
fsm = self.befsm
formula = self.nnf("G (a <-> !b)")
# bound 0
offset = 0
bound = 0
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 0
bound = 1
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# ---- other offset ----
# bound 0
offset = 2
bound = 0
ref_expr= Be.false(self.enc.manager)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 2
bound = 1
ref_expr= ltlspec.bounded_semantics_without_loop_at_offset(fsm, formula, 0, bound, offset) \
|( ltlspec.bounded_semantics_with_loop_at_offset(fsm, formula, 0, bound, 0, offset) \
& bmcutils.loop_condition(self.enc, bound+offset, offset))
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
def test_next(self):
with Configure(self, __file__, "/models/flipflops.smv"):
fsm = self.befsm
formula = self.nnf("X (a <-> !b)")
# bound 0
offset = 0
bound = 0
ref_expr= ltlspec.bounded_semantics(fsm, formula, bound)
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
self.assertEqual(canonical_cnf(expr), canonical_cnf(ref_expr))
# bound 1
offset = 0
bound = 1
# done this way to avoid the depth 1 optimisation
car = Wff.decorate(ltlspec.car(formula)).to_be(fsm.encoding)
ref_expr= self.enc.shift_to_time(car, offset+1) \
| (self.enc.shift_to_time(car, offset) & bmcutils.loop_condition(self.enc, bound, 0))
expr = ltlspec.bounded_semantics_at_offset(fsm, formula, bound, offset)
def test_constraint_context_sigma(self):
fsm = master_be_fsm()
_true = Node.from_ptr(parse_ltl_spec("TRUE"))
_true = bmcutils.make_nnf_boolean_wff(_true)
_truen = _true.to_node()
cond = Wff(parse_ltl_spec("G !(mouse = hover)"))\
.to_boolean_wff()\
.to_negation_normal_form()
off_1 = 0
off_2 = 2
length = 1
# sigma1
problem = diagnosability.generate_sat_problem([], (_truen, _truen),
length, _true,
cond.to_node(), _truen)
tm_cond = ltlspec.bounded_semantics_at_offset(fsm, cond.to_node(),
length, off_1)
canonical_p = tests.canonical_cnf(problem)
canonical_f = tests.canonical_cnf(tm_cond)
self.assertTrue(all(clause in canonical_p for clause in canonical_f))
# sigma2
problem = diagnosability.generate_sat_problem([], (_truen, _truen),
length, _true, _truen,
cond.to_node())
tm_cond = ltlspec.bounded_semantics_at_offset(fsm, cond.to_node(),
length, off_2)
canonical_p = tests.canonical_cnf(problem)
canonical_f = tests.canonical_cnf(tm_cond)
self.assertTrue(all(clause in canonical_p for clause in canonical_f))
def generate_sat_problem(observable_vars, formula_nodes, length, theta, sigma1, sigma2):
"""
Generates a SAT problem which is satisfiable iff the given `formula` is
*NOT* diagnosable for the loaded model for traces of length `length`.
:param observable_vars: the list of the boolean variables that are considered
visible in the scope of this diagnosability test
:param formula: the node (NuSMV ast representation) representing the formula
whose diagnosability is under verification
:param length: the maximum length of the generated traces.
:param theta: the initial condition placed on the initial belief state
(in the form of a :see:`pynusmv.node.Node`)
:param sigma1: the shape of the traces considered relevant for the first
member of the critical pair in the ongoing diagnosability test
(in the form of a :see:`pynusmv.node.Node`)
:param sigma2: the shape of the traces considered relevant for the second
member of the critical pair in the ongoing diagnosability test
(in the form of a :see:`pynusmv.node.Node`)
:return: a SAT problem which is satisfiable iff the given formula is not
diagnosable on the loaded model.
"""
fsm = master_be_fsm()
offset_1 = 0
offset_2 = length + 1
problem = (
generate_path(offset_1, length)
& generate_path(offset_2, length)
& constraint_same_observations(observable_vars, offset_1, offset_2, length)
& constraint_context_theta_initial(theta, offset_1, offset_2)
& bounded_semantics_at_offset(fsm, sigma1, length, offset_1)
& bounded_semantics_at_offset(fsm, sigma2, length, offset_2)
& constraint_eventually_critical_pair(formula_nodes, offset_1, offset_2, length)
)
return problem
