def __init__(self,
lemmatization=False,
granularity="label",
failures_skip=None):
Model.__init__(self, lemmatization)
self.granularity = granularity
self.failures_skip = failures_skip
self.training_dbs = [repository.COMMITS_DB]
self.eval_dbs[repository.COMMITS_DB] = (
repository.COMMITS_DB,
repository.COMMIT_EXPERIENCES_DB,
)
if granularity == "label":
self.training_dbs.append(test_scheduling.TEST_LABEL_SCHEDULING_DB)
self.eval_dbs[test_scheduling.TEST_LABEL_SCHEDULING_DB] = (
test_scheduling.PAST_FAILURES_LABEL_DB,
test_scheduling.FAILING_TOGETHER_LABEL_DB,
)
elif granularity == "group":
self.training_dbs.append(test_scheduling.TEST_GROUP_SCHEDULING_DB)
self.eval_dbs[test_scheduling.TEST_GROUP_SCHEDULING_DB] = (
test_scheduling.PAST_FAILURES_GROUP_DB,
test_scheduling.TOUCHED_TOGETHER_DB,
)
self.cross_validation_enabled = False
self.entire_dataset_training = True
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
test_scheduling_features.prev_failures(),
]
if granularity == "label":
feature_extractors += [
test_scheduling_features.platform(),
# test_scheduling_features.chunk(),
test_scheduling_features.suite(),
]
elif granularity == "group":
feature_extractors += [
test_scheduling_features.path_distance(),
test_scheduling_features.common_path_components(),
test_scheduling_features.touched_together(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, interpretable=False):
CommitModel.__init__(self, lemmatization)
self.required_dbs.append(BUG_INTRODUCING_COMMITS_DB)
self.store_dataset = True
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
commit_features.author_experience(),
commit_features.reviewer_experience(),
commit_features.reviewers_num(),
commit_features.component_touched_prev(),
commit_features.directory_touched_prev(),
commit_features.file_touched_prev(),
commit_features.types(),
commit_features.files(),
commit_features.components(),
commit_features.components_modified_num(),
commit_features.directories(),
commit_features.directories_modified_num(),
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.functions_touched_num(),
commit_features.functions_touched_size(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
column_transformers = [("data", DictVectorizer(), "data")]
if not interpretable:
column_transformers.append(
("desc", self.text_vectorizer(min_df=0.0001), "desc"))
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors,
cleanup_functions),
),
("union", ColumnTransformer(column_transformers)),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization: bool = False) -> None:
CommitModel.__init__(self, lemmatization)
self.calculate_importance = False
self.training_dbs += [bugzilla.BUGS_DB]
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
commit_features.reviewers_num(),
commit_features.types(),
commit_features.files(),
commit_features.components(),
commit_features.components_modified_num(),
commit_features.directories(),
commit_features.directories_modified_num(),
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.functions_touched_num(),
commit_features.functions_touched_size(),
commit_features.source_code_file_metrics(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors,
cleanup_functions),
),
(
"union",
ColumnTransformer([
("data", DictVectorizer(), "data"),
("desc", self.text_vectorizer(min_df=0.0001), "desc"),
]),
),
])
self.clf = xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count())
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
CommitModel.__init__(self, lemmatization)
self.calculate_importance = False
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.file_size(),
commit_features.test_added(),
commit_features.added(),
commit_features.deleted(),
commit_features.test_deleted(),
commit_features.author_experience(),
commit_features.reviewer_experience(),
commit_features.reviewers_num(),
commit_features.component_touched_prev(),
commit_features.directory_touched_prev(),
commit_features.file_touched_prev(),
commit_features.types(),
commit_features.components(),
commit_features.components_modified_num(),
commit_features.directories(),
commit_features.directories_modified_num(),
commit_features.files(),
commit_features.files_modified_num(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"commit_extractor",
commit_features.CommitExtractor(
feature_extractors, cleanup_functions
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("desc", self.text_vectorizer(), "desc"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, granularity="label"):
Model.__init__(self, lemmatization)
self.granularity = granularity
self.required_dbs = [repository.COMMITS_DB]
if granularity == "label":
self.required_dbs.append(test_scheduling.TEST_LABEL_SCHEDULING_DB)
elif granularity == "group":
self.required_dbs.append(test_scheduling.TEST_GROUP_SCHEDULING_DB)
self.cross_validation_enabled = False
self.entire_dataset_training = True
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
test_scheduling_features.name(),
test_scheduling_features.prev_failures(),
]
if granularity == "label":
feature_extractors += [
test_scheduling_features.platform(),
test_scheduling_features.chunk(),
test_scheduling_features.suite(),
]
elif granularity == "group":
feature_extractors += [
test_scheduling_features.path_distance(),
test_scheduling_features.common_path_components(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
CommitModel.__init__(self, lemmatization)
self.training_dbs.append(test_scheduling.TEST_LABEL_SCHEDULING_DB)
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
# commit_features.author_experience(),
# commit_features.reviewer_experience(),
commit_features.reviewers_num(),
# commit_features.component_touched_prev(),
# commit_features.directory_touched_prev(),
# commit_features.file_touched_prev(),
commit_features.types(),
commit_features.files(),
commit_features.components(),
commit_features.components_modified_num(),
commit_features.directories(),
commit_features.directories_modified_num(),
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
]
self.extraction_pipeline = Pipeline(
[
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False):
Model.__init__(self, lemmatization)
self.required_dbs = [
repository.COMMITS_DB, test_scheduling.TEST_SCHEDULING_DB
]
self.calculate_importance = False
self.cross_validation_enabled = False
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
test_scheduling_features.name(),
test_scheduling_features.platform(),
test_scheduling_features.chunk(),
test_scheduling_features.suite(),
test_scheduling_features.prev_failures(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
def __init__(self, lemmatization=False, bug_data=False):
CommitModel.__init__(self, lemmatization, bug_data)
self.calculate_importance = False
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
commit_features.author_experience(),
commit_features.reviewer_experience(),
commit_features.reviewers_num(),
commit_features.component_touched_prev(),
commit_features.directory_touched_prev(),
commit_features.file_touched_prev(),
commit_features.types(),
commit_features.components(),
commit_features.directories(),
commit_features.files(),
]
if bug_data:
feature_extractors += [
bug_features.product(),
bug_features.component(),
bug_features.severity(),
bug_features.priority(),
bug_features.has_crash_signature(),
bug_features.has_regression_range(),
bug_features.whiteboard(),
bug_features.keywords(),
bug_features.number_of_bug_dependencies(),
bug_features.blocked_bugs_number(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
self.extraction_pipeline = Pipeline(
[
(
"commit_extractor",
commit_features.CommitExtractor(
feature_extractors, cleanup_functions
),
),
(
"union",
ColumnTransformer(
[
("data", DictVectorizer(), "data"),
("desc", self.text_vectorizer(), "desc"),
]
),
),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count())
self.clf.set_params(predictor="cpu_predictor")
def __init__(
self,
lemmatization: bool = False,
interpretable: bool = True,
use_finder: bool = False,
exclude_finder: bool = True,
finder_regressions_only: bool = False,
) -> None:
CommitModel.__init__(self, lemmatization)
self.training_dbs += [BUG_INTRODUCING_COMMITS_DB, bugzilla.BUGS_DB]
if finder_regressions_only:
self.training_dbs.append(BUG_FIXING_COMMITS_DB)
self.store_dataset = True
self.sampler = RandomUnderSampler(random_state=0)
self.use_finder = use_finder
self.exclude_finder = exclude_finder
assert (
use_finder ^ exclude_finder
), "Using both use_finder and exclude_finder option does not make a lot of sense"
self.finder_regressions_only = finder_regressions_only
feature_extractors = [
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
commit_features.author_experience(),
commit_features.reviewer_experience(),
commit_features.reviewers_num(),
commit_features.component_touched_prev(),
commit_features.directory_touched_prev(),
commit_features.file_touched_prev(),
commit_features.types(),
commit_features.files(),
commit_features.components(),
commit_features.components_modified_num(),
commit_features.directories(),
commit_features.directories_modified_num(),
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.functions_touched_num(),
commit_features.functions_touched_size(),
commit_features.source_code_file_metrics(),
]
cleanup_functions = [
feature_cleanup.fileref(),
feature_cleanup.url(),
feature_cleanup.synonyms(),
]
column_transformers = [("data", DictVectorizer(), "data")]
if not interpretable:
column_transformers.append(
("desc", self.text_vectorizer(min_df=0.0001), "desc")
)
self.extraction_pipeline = Pipeline(
[
(
"commit_extractor",
commit_features.CommitExtractor(
feature_extractors, cleanup_functions
),
),
("union", ColumnTransformer(column_transformers)),
]
)
self.clf = xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count())
self.clf.set_params(predictor="cpu_predictor")
def __init__(self,
lemmatization=False,
granularity="label",
use_subset=False):
Model.__init__(self, lemmatization)
self.granularity = granularity
# This is useful for development purposes, it avoids using too much memory
# by using a subset of the dataset (dropping some passing runnables).
self.use_subset = use_subset
self.training_dbs = [repository.COMMITS_DB]
self.eval_dbs[repository.COMMITS_DB] = (
repository.COMMITS_DB,
repository.COMMIT_EXPERIENCES_DB,
)
if granularity == "label":
self.training_dbs.append(test_scheduling.TEST_LABEL_SCHEDULING_DB)
self.eval_dbs[test_scheduling.TEST_LABEL_SCHEDULING_DB] = (
test_scheduling.PAST_FAILURES_LABEL_DB,
test_scheduling.FAILING_TOGETHER_LABEL_DB,
)
elif granularity == "group":
self.training_dbs.append(test_scheduling.TEST_GROUP_SCHEDULING_DB)
self.eval_dbs[test_scheduling.TEST_GROUP_SCHEDULING_DB] = (
test_scheduling.PAST_FAILURES_GROUP_DB,
test_scheduling.TOUCHED_TOGETHER_DB,
)
self.cross_validation_enabled = False
self.entire_dataset_training = True
self.sampler = RandomUnderSampler(random_state=0)
feature_extractors = [
test_scheduling_features.prev_failures(),
]
if granularity == "label":
feature_extractors += [
test_scheduling_features.platform(),
# test_scheduling_features.chunk(),
test_scheduling_features.suite(),
]
elif granularity == "group":
feature_extractors += [
commit_features.source_code_files_modified_num(),
commit_features.other_files_modified_num(),
commit_features.test_files_modified_num(),
commit_features.source_code_file_size(),
commit_features.other_file_size(),
commit_features.test_file_size(),
commit_features.source_code_added(),
commit_features.other_added(),
commit_features.test_added(),
commit_features.source_code_deleted(),
commit_features.other_deleted(),
commit_features.test_deleted(),
test_scheduling_features.path_distance(),
test_scheduling_features.common_path_components(),
test_scheduling_features.touched_together(),
]
self.extraction_pipeline = Pipeline([
(
"commit_extractor",
commit_features.CommitExtractor(feature_extractors, []),
),
("union", ColumnTransformer([("data", DictVectorizer(), "data")])),
])
self.clf = xgboost.XGBClassifier(n_jobs=16)
self.clf.set_params(predictor="cpu_predictor")
