def transfer_lessons4():
data = DefectData.get_all_projects()["Apache"]
for proj, paths in data.iteritems():
if not proj in paths.bellw:
res = {proj[:6]: []}
"If training data.dat doesn't exist, create it."
pred, pred2, distr, distr2 = [], [], [], []
if not "train_bellw" in locals():
train_bellw = list2dataframe(data[paths.bellw].data)
train_local = list2dataframe(paths.data[:-1])
test = list2dataframe(paths.data[-1])
patched_local = xtree.execute(train_local, test)
patched_bellw = xtree.execute(train_bellw, test)
pred, distr = rforest(train_bellw,
patched_local) # How good are the patches
pred2, distr2 = rforest(train_bellw,
patched_bellw) # How good are the predcitions
pred3, distr3 = rforest(train_bellw,
test) # How good are the predcitions
res[proj[:6]].extend(pred_stats(before=test[test.columns[-1]],
after=pred3,
distr=distr3))
res[proj[:6]].append(impact(test, pred))
res[proj[:6]].append(impact(test, pred2))
yield res
def transfer_lessons3():
data = DefectData.get_all_projects()["Apache"]
for proj, paths in data.iteritems():
if not proj in paths.bellw:
res = {proj[:6]: []}
"If training data.dat doesn't exist, create it."
pred, pred2, distr, distr2 = [], [], [], []
if not "train" in locals():
train = list2dataframe(data[paths.bellw].data)
test, validation = train_test_split(list2dataframe(paths.data),
test_size=0.8)
# test = list2dataframe(paths.data.dat[-1])
# validation = list2dataframe(paths.data.dat[:-1])
patched = xtree.execute(train, test)
a, b = rforest(train, patched) # How good are the patches
aa, bb = rforest(train, test) # How good are the predcitions
pred.append(a)
pred2.append(aa)
distr.append(b)
distr2.append(bb)
res[proj[:6]].extend(pred_stats(before=test[test.columns[-1]],
after=pred2,
distr=distr2))
res[proj[:6]].extend(impact(test, pred))
yield res
def transfer_lessons(data=None):
if data is None:
data = DefectData.get_all_projects()["Apache"]
for proj, paths in data.iteritems():
"Make sure we don't test on the bellwether dataset"
if not proj in paths.bellw:
res = {proj[:6]: {
"pd": [],
"pf": [],
"local": [],
"bellw": []}
}
bellw = list2dataframe(data[paths.bellw].data)
for train_bellw, validation in CrossValidation.split(bellw,
ways=5):
train_local = list2dataframe(paths.data[:-1])
test = list2dataframe(paths.data[-1])
patched_local = xtree(train_local, test)
patched_bellw = xtree(train_bellw, list2dataframe(paths.data))
# How good are the patches from local lessons?
pred_local, distr_local = xgboost(validation, patched_local)
# How good are the patches from the bellwether lessons?
pred_bellw, distr_bellw = xgboost(validation, patched_bellw)
# How good are the predictions
pred_qual, distr_qual = xgboost(validation, test)
pred = pred_stats(before=test[test.columns[-1]],
after=pred_qual,
distr=distr_qual)
res[proj[:6]]["pd"].append(pred[0])
res[proj[:6]]["pf"].append(pred[1])
res[proj[:6]]["local"].append(impact(test, pred_local))
res[proj[:6]]["bellw"].append(impact(test, pred_bellw))
yield res
def transfer_lessons2(n_folds=1):
data = DefectData.get_all_projects()["Apache"]
print("Name\tPd\tPf\tImprovement")
for proj, paths in data.iteritems():
if not proj in paths.bellw:
print(proj[:4], end="\t")
"If training data.dat doesn't exist, create it."
train, validation = train_test_split(list2dataframe(paths.data),
test_size=0.8)
test = paths.data[-1]
validation = paths.data[:-1]
patched = xtree.execute(train, test)
test = list2dataframe(test)
pred, distr = rforest(validation,
patched) # How good are the patches
pred2, distr2 = rforest(validation,
test) # How good are the predcitions
pred_stats(before=test[test.columns[-1]],
after=pred2,
distr=distr2)
impact(test, pred)
def transfer_lessons(data=None):
if data is None:
data = DefectData.get_all_projects()["Apache"]
for proj, paths in data.iteritems():
"Make sure we don't test on the bellwether dataset"
if proj in ["ant", "ivy", "poi", "jedit"]:
res = {
proj[:6]: {
"xtree_local": [],
"xtree_bellw": [],
"alves": [],
"olive": [],
"shatw": []
}
}
bellw = list2dataframe(data[paths.bellw].data)
test = list2dataframe(paths.data)
test_local = list2dataframe(paths.data[-1])
train_local = list2dataframe(paths.data[:-1])
for train_bellw, validation in CrossValidation.split(bellw,
ways=5):
patched_alves = alves(train_bellw, test_local)
patched_shatw = shatnawi(train_bellw, test_local)
patched_olive = oliveira(train_bellw, test_local)
patched_xtree = xtree(train_bellw, test_local)
patched_xtree_local = xtree(train_local, test_local)
# How good are the patches from Alves?
pred_alves, distr_alves = xgboost(validation, patched_alves)
# How good are the patches from Shatnawi?
pred_shatw, distr_shatw = xgboost(validation, patched_shatw)
# How good are the patches from Oliveira?
pred_olive, distr_olive = xgboost(validation, patched_olive)
# How good are the patches from the bellwether XTREE?
pred_xtree, distr_xtree = xgboost(validation, patched_xtree)
# How good are the patches from the local XTREE?
pred_xtree_local, distr_xtree_local = xgboost(
validation, patched_xtree_local)
res[proj[:6]]["alves"].append(impact(test, pred_alves))
res[proj[:6]]["shatw"].append(impact(test, pred_shatw))
res[proj[:6]]["olive"].append(impact(test, pred_olive))
res[proj[:6]]["xtree_bellw"].append(impact(test, pred_xtree))
res[proj[:6]]["xtree_local"].append(
impact(test, pred_xtree_local))
# Not yet...
# # How good are the patches from the bellwether lessons?
# pred_fontana, distr_fontana = xgboost(validation, patched_xtree)
#
# # res[proj[:6]]["fontana"].append(pred[1])
yield res