def get_nextcase(self):
# construct directives
NNcases = []
while len(self.casenums) < State.cases['size']:
casenum, casenumseq, directs, objs = get_directs(
self.selectfunc, self.prefunc, self.postfunc)
predict = None
if casenum in self.casenums or casenum in NNcases:
continue
elif len(self.casenums) < self.NN_min_dataset_size or len(
self.ranking) == 0:
break
else:
NNcases.append((casenum, casenumseq, directs, objs))
if len(NNcases) < self.NN_basket_size: continue
casenum, casenumseq, directs, objs, predict = self.NN_get_nextcase(
NNcases)
break
if casenum in self.casenums: continue
break
self.casenums.append(casenum)
return Case(self,
casenum=casenum,
caseorder=len(self.casenums),
directs=directs,
caseidxseq=casenumseq,
objs=objs,
predict=predict)
def __init__(self):
self.refcase = None
self.ranking = [] # (casenum, caseorder, performance )
self.failed = [] # (casenum, caseorder, performance )
self.casenums = []
# configure for case generation
if State.direct.has_key('casegen'):
if len(State.direct['casegen'])!=1:
raise UserException('Only one CASEGEN directive is allowed')
sub = State.direct['casegen'][0][0] # (sub, stmt, span)
subsub = sub.cases[0][0][0]
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
if item=='rand':
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_random
elif item=='dtree':
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_dtree
else: raise UserException('%s is not valid cage generation algorithm'%item)
else:
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_random
# configure for measurment
self.measure = {}
for sub, stmt, span in State.direct['measure']:
for subsub in sub.cases[0][0]:
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
self.measure[item] = attrs
# configure for verification
self.verify = {}
for sub, stmt, span in State.direct['verify']:
for subsub in sub.cases[0][0]:
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
self.verify[item] = attrs
self.rank_var = State.direct['rank'][0][0].cases[0][0][0].case[0][0][0]
self.rank_attrs = State.direct['rank'][0][0].cases[0][0][0].case[0][1]
# build Neural Network
casenum, casenumseq, directs, objs = get_directs( self.selectfunc, self.prefunc, self.postfunc)
self.casesizes = []
for caseidx, caseobj in casenumseq:
self.casesizes.append(max(1, int(math.ceil(math.log(caseobj.size, 2)))))
self.NN_input_size = sum(self.casesizes)
self.NN_hidden_layers = 4
self.NN_target_size = 1
self.NN_net = buildNetwork( self.NN_input_size, self.NN_hidden_layers, self.NN_target_size, bias = True )
self.NN_trainer = BackpropTrainer(self.NN_net, momentum=0.1, weightdecay=0.01, learningrate=0.01)
self.NN_ds = SupervisedDataSet( self.NN_input_size, self.NN_target_size )
self.NN_basket_size = 1000
self.NN_min_dataset_size = min(20, State.cases['size'])
self.NN_amp_factor = 1.0
def get_nextcase(self):
# construct directives
NNcases = []
while len(self.casenums)<State.cases['size']:
casenum, casenumseq, directs, objs = get_directs( self.selectfunc, self.prefunc, self.postfunc)
predict = None
if casenum in self.casenums or casenum in NNcases:
continue
elif len(self.casenums)<self.NN_min_dataset_size or len(self.ranking)==0:
break
else:
NNcases.append((casenum, casenumseq, directs, objs))
if len(NNcases)<self.NN_basket_size: continue
casenum, casenumseq, directs, objs, predict = self.NN_get_nextcase(NNcases)
break
if casenum in self.casenums: continue
break
self.casenums.append(casenum)
return Case( self, casenum=casenum, caseorder=len(self.casenums), directs=directs, caseidxseq=casenumseq, objs=objs, predict=predict )
def __init__(self):
self.refcase = None
self.ranking = [] # (casenum, caseorder, performance )
self.failed = [] # (casenum, caseorder, performance )
self.casenums = []
# configure for case generation
if State.direct.has_key('casegen'):
if len(State.direct['casegen']) != 1:
raise UserException('Only one CASEGEN directive is allowed')
sub = State.direct['casegen'][0][0] # (sub, stmt, span)
subsub = sub.cases[0][0][0]
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
if item == 'rand':
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_random
elif item == 'dtree':
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_dtree
else:
raise UserException(
'%s is not valid cage generation algorithm' % item)
else:
self.selectfunc, self.prefunc, self.postfunc, self.updatefunc = casegen_functions_random
# configure for measurment
self.measure = {}
for sub, stmt, span in State.direct['measure']:
for subsub in sub.cases[0][0]:
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
self.measure[item] = attrs
# configure for verification
self.verify = {}
for sub, stmt, span in State.direct['verify']:
for subsub in sub.cases[0][0]:
item = SrcFile.applymap(subsub.case[0][0][0])
attrs = SrcFile.applymap(subsub.case[0][1])
self.verify[item] = attrs
self.rank_var = State.direct['rank'][0][0].cases[0][0][0].case[0][0][0]
self.rank_attrs = State.direct['rank'][0][0].cases[0][0][0].case[0][1]
# build Neural Network
casenum, casenumseq, directs, objs = get_directs(
self.selectfunc, self.prefunc, self.postfunc)
self.casesizes = []
for caseidx, caseobj in casenumseq:
self.casesizes.append(
max(1, int(math.ceil(math.log(caseobj.size, 2)))))
self.NN_input_size = sum(self.casesizes)
self.NN_hidden_layers = 4
self.NN_target_size = 1
self.NN_net = buildNetwork(self.NN_input_size,
self.NN_hidden_layers,
self.NN_target_size,
bias=True)
self.NN_trainer = BackpropTrainer(self.NN_net,
momentum=0.1,
weightdecay=0.01,
learningrate=0.01)
self.NN_ds = SupervisedDataSet(self.NN_input_size, self.NN_target_size)
self.NN_basket_size = 1000
self.NN_min_dataset_size = min(20, State.cases['size'])
self.NN_amp_factor = 1.0
