зеркало из https://github.com/mozilla/bugbug.git
Refactor print statements to use logger (#3330)
This commit is contained in:
ElusiveEllie
GitHub
Родитель
294286ae47
Коммит
8936218ab2
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@ -3,11 +3,16 @@
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# License, v. 2.0. If a copy of the MPL was not distributed with this file,
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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from logging import INFO, basicConfig, getLogger
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import dateutil.parser
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from dateutil.relativedelta import relativedelta
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from bugbug import bugzilla
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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def bool_str(val):
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assert val in ["", "0", "1"], f"Unexpected boolean value: '{val}'"
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@ -536,7 +541,7 @@ def rollback(bug, when=None, do_assert=False):
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if do_assert:
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assert False, msg
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else:
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print(msg)
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logger.error(msg)
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def parse_flag_change(change):
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parts = change.split("(")
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@ -869,9 +874,8 @@ def get_inconsistencies(bugs):
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for bug in bugs:
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try:
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rollback(bug, do_assert=True)
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except Exception as e:
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print(bug["id"])
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print(e)
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except Exception:
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logger.exception("Failed to rollback bug %s", bug["id"])
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inconsistencies.append(bug)
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return inconsistencies
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@ -888,6 +892,6 @@ if __name__ == "__main__":
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for bug in tqdm(bugzilla.get_bugs()):
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if args.verbose:
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print(bug["id"])
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logger.info(bug["id"])
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rollback(bug, do_assert=True)
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@ -7,6 +7,7 @@ import collections
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import csv
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import re
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from datetime import datetime
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from logging import INFO, basicConfig, getLogger
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from typing import Iterable, Iterator, NewType, Optional
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import tenacity
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@ -16,6 +17,9 @@ from tqdm import tqdm
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from bugbug import db, utils
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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BugDict = NewType("BugDict", dict)
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BUGS_DB = "data/bugs.json"
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@ -191,7 +195,7 @@ def download_bugs(bug_ids: Iterable[int], security: bool = False) -> list[BugDic
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old_bug_count += 1
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new_bug_ids_set.discard(int(bug["id"]))
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print(f"Loaded {old_bug_count} bugs.")
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logger.info("Loaded %d bugs.", old_bug_count)
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new_bug_ids = sorted(list(new_bug_ids_set))
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@ -417,8 +421,11 @@ def calculate_maintenance_effectiveness_indicator(
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"closed": {},
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}
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print(
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f"Calculating maintenance effectiveness indicator for the {team} team from {from_date} to {to_date}"
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logger.info(
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"Calculating maintenance effectiveness indicator for the %s team from %s to %s",
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team,
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from_date,
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to_date,
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)
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for severity in MAINTENANCE_EFFECTIVENESS_SEVERITY_WEIGHTS.keys():
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@ -5,6 +5,7 @@
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import pickle
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from collections import defaultdict
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from logging import INFO, basicConfig, getLogger
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from typing import Any
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import matplotlib
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@ -29,6 +30,9 @@ from bugbug.github import Github
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from bugbug.nlp import SpacyVectorizer
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from bugbug.utils import split_tuple_generator, to_array
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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def classification_report_imbalanced_values(
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y_true, y_pred, labels, target_names=None, sample_weight=None, digits=2, alpha=0.1
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@ -398,7 +402,7 @@ class Model:
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self.clf.fit(X_train, self.le.transform(y_train))
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print("Model trained")
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logger.info("Model trained")
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feature_names = self.get_human_readable_feature_names()
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if self.calculate_importance and len(feature_names):
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@ -3,6 +3,8 @@
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# License, v. 2.0. If a copy of the MPL was not distributed with this file,
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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from logging import INFO, basicConfig, getLogger
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import xgboost
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from imblearn.under_sampling import RandomUnderSampler
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from sklearn.compose import ColumnTransformer
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@ -12,6 +14,9 @@ from sklearn.pipeline import Pipeline
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from bugbug import bugzilla, commit_features, feature_cleanup, labels, repository, utils
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from bugbug.model import CommitModel
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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class AnnotateIgnoreModel(CommitModel):
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def __init__(self, lemmatization: bool = False) -> None:
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@ -105,16 +110,14 @@ class AnnotateIgnoreModel(CommitModel):
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for node, label in labels.get_labels("annotateignore"):
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classes[node] = int(label)
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print(
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"{} commits that can be ignored".format(
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sum(1 for label in classes.values() if label == 1)
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)
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logger.info(
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"%d commits that can be ignored",
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sum(1 for label in classes.values() if label == 1),
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)
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print(
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"{} commits that cannot be ignored".format(
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sum(1 for label in classes.values() if label == 0)
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)
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logger.info(
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"%d commits that cannot be ignored",
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sum(1 for label in classes.values() if label == 0),
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)
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return classes, [0, 1]
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@ -4,6 +4,7 @@
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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from collections import Counter
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from logging import INFO, basicConfig, getLogger
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import xgboost
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from sklearn.compose import ColumnTransformer
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@ -24,6 +25,9 @@ ADDRESSES_TO_EXCLUDE = [
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"nobody@t4b.me",
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]
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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class AssigneeModel(BugModel):
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def __init__(self, lemmatization=False):
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@ -100,9 +104,9 @@ class AssigneeModel(BugModel):
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if count > MINIMUM_ASSIGNMENTS
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)
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print(f"{len(top_assignees)} assignees")
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logger.info("%d assignees", len(top_assignees))
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for assignee, count in assignee_counts:
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print(f"{assignee}: {count}")
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logger.info("%s: %d", assignee, count)
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classes = {
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bug_id: assignee
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@ -4,6 +4,7 @@
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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from datetime import datetime
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from logging import INFO, basicConfig, getLogger
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import dateutil.parser
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import xgboost
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@ -16,6 +17,9 @@ from sklearn.pipeline import Pipeline
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from bugbug import bug_features, commit_features, feature_cleanup, repository, utils
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from bugbug.model import CommitModel
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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class BackoutModel(CommitModel):
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def __init__(self, lemmatization=False, bug_data=False):
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@ -107,15 +111,13 @@ class BackoutModel(CommitModel):
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classes[commit_data["node"]] = 1 if commit_data["backedoutby"] else 0
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print(
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"{} commits were backed out".format(
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sum(1 for label in classes.values() if label == 1)
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)
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logger.info(
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"%d commits were backed out",
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sum(1 for label in classes.values() if label == 1),
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)
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print(
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"{} commits were not backed out".format(
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sum(1 for label in classes.values() if label == 0)
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)
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logger.info(
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"%d commits were not backed out",
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sum(1 for label in classes.values() if label == 0),
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)
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return classes, [0, 1]
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@ -5,6 +5,7 @@
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from collections import Counter
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from datetime import datetime, timezone
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from logging import INFO, basicConfig, getLogger
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import dateutil.parser
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import xgboost
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@ -17,6 +18,9 @@ from bugbug import bug_features, bugzilla, feature_cleanup, utils
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from bugbug.bugzilla import get_product_component_count
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from bugbug.model import BugModel
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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class ComponentModel(BugModel):
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PRODUCTS = {
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@ -164,9 +168,9 @@ class ComponentModel(BugModel):
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component_counts = Counter(classes.values()).most_common()
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top_components = set(component for component, count in component_counts)
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print(f"{len(top_components)} components")
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logger.info("%d components", len(top_components))
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for component, count in component_counts:
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print(f"{component}: {count}")
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logger.info("%s: %d", component, count)
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# Assert there is at least one bug for each conflated component.
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for conflated_component in self.CONFLATED_COMPONENTS:
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@ -242,14 +246,18 @@ class ComponentModel(BugModel):
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full_comp = f"{product}::{component}"
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if full_comp not in bugs_number.keys():
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print(
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f"Component {component!r} of product {product!r} doesn't exists, failure"
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logger.warning(
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"Component %r of product %r doesn't exists, failure",
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component,
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product,
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)
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success = False
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elif bugs_number[full_comp] <= 0:
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print(
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f"Component {component!r} of product {product!r} have 0 bugs or less in it, failure"
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logger.warning(
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"Component %r of product %r have 0 bugs or less in it, failure",
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component,
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product,
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)
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success = False
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@ -265,7 +273,7 @@ class ComponentModel(BugModel):
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]
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if not matching_components:
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print(f"{conflated_component} doesn't match any component")
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logger.warning("%s doesn't match any component", conflated_component)
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success = False
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continue
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@ -276,8 +284,9 @@ class ComponentModel(BugModel):
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]
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if not matching_components_values:
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print(
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f"{conflated_component} should match at least one component with more than 0 bugs"
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logger.warning(
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"%s should match at least one component with more than 0 bugs",
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conflated_component,
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)
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success = False
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@ -286,13 +295,15 @@ class ComponentModel(BugModel):
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for full_comp in self.CONFLATED_COMPONENTS_MAPPING.values():
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if full_comp not in bugs_number:
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print(
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f"{full_comp} from conflated component mapping doesn't exists, failure"
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logger.warning(
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"%s from conflated component mapping doesn't exists, failure",
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full_comp,
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)
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success = False
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elif bugs_number[full_comp] <= 0:
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print(
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f"{full_comp} from conflated component mapping have less than 1 bug, failure"
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logger.warning(
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"%s from conflated component mapping have less than 1 bug, failure",
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full_comp,
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)
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success = False
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@ -309,7 +320,7 @@ class ComponentModel(BugModel):
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]
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if not (matching_components or in_mapping):
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print(f"It should be possible to map {conflated_component}")
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logger.warning("It should be possible to map %s", conflated_component)
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success = False
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continue
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@ -336,15 +347,16 @@ class ComponentModel(BugModel):
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if not meaningful_product_components.issubset(
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self.meaningful_product_components
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):
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print("Meaningful product components mismatch")
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logger.warning("Meaningful product components mismatch")
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new_meaningful_product_components = (
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meaningful_product_components.difference(
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self.meaningful_product_components
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)
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)
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print(
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f"New meaningful product components {new_meaningful_product_components!r}"
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logger.info(
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"New meaningful product components %r",
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new_meaningful_product_components,
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)
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success = False
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|
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@ -4,6 +4,7 @@
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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import itertools
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from logging import INFO, basicConfig, getLogger
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from typing import Any
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import xgboost
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@ -15,6 +16,9 @@ from sklearn.pipeline import Pipeline
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from bugbug import bug_features, bugzilla, feature_cleanup, labels, utils
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from bugbug.model import BugModel
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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class DefectModel(BugModel):
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def __init__(self, lemmatization=False, historical=False):
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@ -254,8 +258,8 @@ class DefectModel(BugModel):
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def get_labels(self) -> tuple[dict[int, Any], list[Any]]:
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classes = self.get_bugbug_labels("bug")
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print("{} bugs".format(sum(1 for label in classes.values() if label == 1)))
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print("{} non-bugs".format(sum(1 for label in classes.values() if label == 0)))
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logger.info("%d bugs", (sum(1 for label in classes.values() if label == 1)))
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logger.info("%d non-bugs", (sum(1 for label in classes.values() if label == 0)))
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return classes, [0, 1]
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|
|
|
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|
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@ -3,10 +3,14 @@
|
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# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
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from typing import Any
|
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|
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from bugbug.models.defect import DefectModel
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|
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basicConfig(level=INFO)
|
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logger = getLogger(__name__)
|
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|
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|
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class DefectEnhancementTaskModel(DefectModel):
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def __init__(self, lemmatization=False, historical=False):
|
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|
|
@ -17,19 +21,14 @@ class DefectEnhancementTaskModel(DefectModel):
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def get_labels(self) -> tuple[dict[int, Any], list[Any]]:
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classes = self.get_bugbug_labels("defect_enhancement_task")
|
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|
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print(
|
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"{} defects".format(
|
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sum(1 for label in classes.values() if label == "defect")
|
||||
)
|
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logger.info(
|
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"%d defects", sum(1 for label in classes.values() if label == "defect")
|
||||
)
|
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print(
|
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"{} enhancements".format(
|
||||
sum(1 for label in classes.values() if label == "enhancement")
|
||||
)
|
||||
)
|
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print(
|
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"{} tasks".format(sum(1 for label in classes.values() if label == "task"))
|
||||
logger.info(
|
||||
"%d enhancements",
|
||||
sum(1 for label in classes.values() if label == "enhancement"),
|
||||
)
|
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logger.info("%d tasks", sum(1 for label in classes.values() if label == "task"))
|
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|
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return classes, ["defect", "enhancement", "task"]
|
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|
||||
|
|
|
|||
|
|
@ -3,10 +3,14 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
from typing import Any
|
||||
|
||||
from bugbug.models.defect import DefectModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
class RegressionModel(DefectModel):
|
||||
def __init__(self, lemmatization=False, historical=False):
|
||||
|
|
@ -16,15 +20,11 @@ class RegressionModel(DefectModel):
|
|||
def get_labels(self) -> tuple[dict[int, Any], list[int]]:
|
||||
classes = self.get_bugbug_labels("regression")
|
||||
|
||||
print(
|
||||
"{} regression bugs".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d regression bugs", sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
print(
|
||||
"{} non-regression bugs".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d non-regression bugs", sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import xgboost
|
||||
from imblearn.under_sampling import RandomUnderSampler
|
||||
from sklearn.compose import ColumnTransformer
|
||||
|
|
@ -12,6 +14,9 @@ from sklearn.pipeline import Pipeline
|
|||
from bugbug import bug_features, bugzilla, feature_cleanup, utils
|
||||
from bugbug.model import BugModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
class RegressionRangeModel(BugModel):
|
||||
def __init__(self, lemmatization=False):
|
||||
|
|
@ -73,15 +78,13 @@ class RegressionRangeModel(BugModel):
|
|||
classes[bug_id] = 1
|
||||
elif bug_data["cf_has_regression_range"] == "no":
|
||||
classes[bug_id] = 0
|
||||
print(
|
||||
"{} bugs have regression range".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs have regression range",
|
||||
sum(1 for label in classes.values() if label == 1),
|
||||
)
|
||||
print(
|
||||
"{} bugs don't have a regression range".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs don't have a regression range",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
|
||||
import itertools
|
||||
from datetime import datetime
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import dateutil.parser
|
||||
import numpy as np
|
||||
|
|
@ -18,6 +19,9 @@ from sklearn.pipeline import Pipeline
|
|||
from bugbug import bugzilla, commit_features, db, feature_cleanup, repository, utils
|
||||
from bugbug.model import CommitModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
BUG_FIXING_COMMITS_DB = "data/bug_fixing_commits.json"
|
||||
db.register(
|
||||
BUG_FIXING_COMMITS_DB,
|
||||
|
|
@ -188,16 +192,14 @@ class RegressorModel(CommitModel):
|
|||
|
||||
classes[node] = 0
|
||||
|
||||
print(
|
||||
"{} commits caused regressions".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d commits caused regressions",
|
||||
sum(1 for label in classes.values() if label == 1),
|
||||
)
|
||||
|
||||
print(
|
||||
"{} commits did not cause regressions".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d commits did not cause regressions",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
@ -224,9 +226,9 @@ class RegressorModel(CommitModel):
|
|||
|
||||
commits.append(commit_data)
|
||||
|
||||
print(f"{len(commits)} commits in the evaluation set")
|
||||
logger.info("%d commits in the evaluation set", len(commits))
|
||||
bugs_num = len(set(commit["bug_id"] for commit in commits))
|
||||
print(f"{bugs_num} bugs in the evaluation set")
|
||||
logger.info("%d bugs in the evaluation set", bugs_num)
|
||||
|
||||
# Sort commits by bug ID, so we can use itertools.groupby to group them by bug ID.
|
||||
commits.sort(key=lambda x: x["bug_id"])
|
||||
|
|
@ -247,7 +249,7 @@ class RegressorModel(CommitModel):
|
|||
total_regressions = sum(1 for _, is_reg in results if is_reg)
|
||||
average_regression_rate = total_regressions / total_landings
|
||||
|
||||
print(f"Average risk is {average_regression_rate}")
|
||||
logger.info("Average risk is %d", average_regression_rate)
|
||||
|
||||
MIN_SAMPLE = 200
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import xgboost
|
||||
from imblearn.over_sampling import BorderlineSMOTE
|
||||
from sklearn.compose import ColumnTransformer
|
||||
|
|
@ -12,6 +14,9 @@ from sklearn.pipeline import Pipeline
|
|||
from bugbug import bug_features, bugzilla, feature_cleanup, utils
|
||||
from bugbug.model import BugModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
class SpamBugModel(BugModel):
|
||||
def __init__(self, lemmatization=False):
|
||||
|
|
@ -105,15 +110,13 @@ class SpamBugModel(BugModel):
|
|||
elif bug_data["product"] == "Invalid Bugs":
|
||||
classes[bug_id] = 1
|
||||
|
||||
print(
|
||||
"{} bugs are classified as non-spam".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs are classified as non-spam",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
print(
|
||||
"{} bugs are classified as spam".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs are classified as spam",
|
||||
sum(1 for label in classes.values() if label == 1),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import xgboost
|
||||
from imblearn.under_sampling import RandomUnderSampler
|
||||
from sklearn.compose import ColumnTransformer
|
||||
|
|
@ -12,6 +14,9 @@ from sklearn.pipeline import Pipeline
|
|||
from bugbug import bug_features, bugzilla, feature_cleanup, utils
|
||||
from bugbug.model import BugModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
class StepsToReproduceModel(BugModel):
|
||||
def __init__(self, lemmatization=False):
|
||||
|
|
@ -78,15 +83,13 @@ class StepsToReproduceModel(BugModel):
|
|||
if change["removed"].startswith("stepswanted"):
|
||||
classes[int(bug_data["id"])] = 1
|
||||
|
||||
print(
|
||||
"{} bugs have no steps to reproduce".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs have no steps to reproduce",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
print(
|
||||
"{} bugs have steps to reproduce".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d bugs have steps to reproduce",
|
||||
sum(1 for label in classes.values() if label == 1),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import xgboost
|
||||
from imblearn.under_sampling import RandomUnderSampler
|
||||
from sklearn.compose import ColumnTransformer
|
||||
|
|
@ -12,6 +14,9 @@ from sklearn.pipeline import Pipeline
|
|||
from bugbug import commit_features, repository, test_scheduling, utils
|
||||
from bugbug.model import CommitModel
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
class TestFailureModel(CommitModel):
|
||||
def __init__(self, lemmatization=False):
|
||||
|
|
@ -96,15 +101,12 @@ class TestFailureModel(CommitModel):
|
|||
else:
|
||||
classes[rev] = 0
|
||||
|
||||
print(
|
||||
"{} commits failed".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d commits failed", sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
print(
|
||||
"{} commits did not fail".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d commits did not fail",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
|
|||
|
|
@ -31,6 +31,7 @@ from bugbug import (
|
|||
)
|
||||
from bugbug.model import Model
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
|
|
@ -497,8 +498,10 @@ class TestSelectModel(Model):
|
|||
len(push["failures"]) + len(push["passes"])
|
||||
for push in pushes[:train_push_len]
|
||||
)
|
||||
print(
|
||||
f"{train_push_len} pushes in the training set (corresponding to {train_len} push/jobs)"
|
||||
logger.info(
|
||||
"%d pushes in the training set (corresponding to %d push/jobs)",
|
||||
train_push_len,
|
||||
train_len,
|
||||
)
|
||||
return X[:train_len], X[train_len:], y[:train_len], y[train_len:]
|
||||
|
||||
|
|
@ -534,21 +537,17 @@ class TestSelectModel(Model):
|
|||
for name in push["passes"]:
|
||||
classes[(push["revs"][0], name)] = 0
|
||||
|
||||
print("{} pushes considered".format(len(pushes)))
|
||||
print(
|
||||
"{} pushes with at least one failure".format(
|
||||
sum(1 for push in pushes if len(push["failures"]) > 0)
|
||||
)
|
||||
logger.info("%d pushes considered", len(pushes))
|
||||
logger.info(
|
||||
"%d pushes with at least one failure",
|
||||
sum(1 for push in pushes if len(push["failures"]) > 0),
|
||||
)
|
||||
print(
|
||||
"{} push/jobs failed".format(
|
||||
sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
logger.info(
|
||||
"%d push/jobs failed", sum(1 for label in classes.values() if label == 1)
|
||||
)
|
||||
print(
|
||||
"{} push/jobs did not fail".format(
|
||||
sum(1 for label in classes.values() if label == 0)
|
||||
)
|
||||
logger.info(
|
||||
"%d push/jobs did not fail",
|
||||
sum(1 for label in classes.values() if label == 0),
|
||||
)
|
||||
|
||||
return classes, [0, 1]
|
||||
|
|
@ -595,7 +594,7 @@ class TestSelectModel(Model):
|
|||
# To evaluate the model with reductions enabled, we need to regenerate the failing together DB, using
|
||||
# only failure data from the training pushes (otherwise, we'd leak training information into the test
|
||||
# set).
|
||||
print("Generate failing together DB (restricted to training pushes)")
|
||||
logger.info("Generate failing together DB (restricted to training pushes)")
|
||||
push_data_iter, push_data_count, _ = test_scheduling.get_push_data(
|
||||
"label" if self.granularity == "label" else "config_group"
|
||||
)
|
||||
|
|
@ -644,12 +643,16 @@ class TestSelectModel(Model):
|
|||
for push in test_pushes.values()
|
||||
if "config_group_failures" not in push
|
||||
)
|
||||
print(
|
||||
f"{missing_config_group_failures} pushes without config_group failures"
|
||||
logger.info(
|
||||
"%d pushes without config_group failures", missing_config_group_failures
|
||||
)
|
||||
|
||||
print(
|
||||
f"Testing on {len(test_pushes)} ({test_pushes_failures} with failures) out of {len(pushes)}. {len(all_tasks)} schedulable tasks."
|
||||
logger.info(
|
||||
"Testing on %d (%d with failures) out of %d. %d schedulable tasks.",
|
||||
len(test_pushes),
|
||||
test_pushes_failures,
|
||||
len(pushes),
|
||||
len(all_tasks),
|
||||
)
|
||||
|
||||
del pushes
|
||||
|
|
@ -705,10 +708,8 @@ class TestSelectModel(Model):
|
|||
for future in concurrent.futures.as_completed(futures):
|
||||
exc = future.exception()
|
||||
if exc is not None:
|
||||
print(
|
||||
"Exception {} while running {}".format(
|
||||
exc, futures[future]["revs"][0]
|
||||
)
|
||||
logger.error(
|
||||
"Exception %s while running %s", exc, futures[future]["revs"][0]
|
||||
)
|
||||
for f in futures:
|
||||
f.cancel()
|
||||
|
|
@ -833,9 +834,11 @@ class TestSelectModel(Model):
|
|||
and result["caught_percentage_config_group"] is not None
|
||||
)
|
||||
|
||||
message += f" In {percentage_caught_one_config_group}% of pushes we caught at least one config/group failure. On average, we caught {average_caught_percentage_config_group}% of all seen config/group failures."
|
||||
|
||||
print(message)
|
||||
logger.info(
|
||||
"In %d%% of pushes we caught at least one config/group failure. On average, we caught %f%% of all seen config/group failures.",
|
||||
percentage_caught_one_config_group,
|
||||
average_caught_percentage_config_group,
|
||||
)
|
||||
|
||||
with concurrent.futures.ProcessPoolExecutor(
|
||||
max_workers=utils.get_physical_cpu_count(),
|
||||
|
|
|
|||
|
|
@ -6,11 +6,15 @@
|
|||
import sys
|
||||
from collections import defaultdict
|
||||
from functools import lru_cache
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import numpy as np
|
||||
from sklearn.base import BaseEstimator, TransformerMixin
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
HAS_OPTIONAL_DEPENDENCIES = False
|
||||
|
||||
try:
|
||||
|
|
@ -26,11 +30,10 @@ try:
|
|||
if HAS_OPTIONAL_DEPENDENCIES:
|
||||
nlp = spacy.load("en_core_web_sm")
|
||||
except OSError:
|
||||
msg = (
|
||||
"Spacy model is missing, install it with: "
|
||||
f"{sys.executable} -m spacy download en_core_web_sm"
|
||||
logger.error(
|
||||
"Spacy model is missing, install it with: %s -m spacy download en_core_web_sm",
|
||||
sys.executable,
|
||||
)
|
||||
print(msg, file=sys.stderr)
|
||||
|
||||
OPT_MSG_MISSING = (
|
||||
"Optional dependencies are missing, install them with: pip install bugbug[nlp]\n"
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from tqdm import tqdm
|
|||
from bugbug import db
|
||||
from bugbug.db import LastModifiedNotAvailable
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
RevisionDict = NewType("RevisionDict", dict)
|
||||
|
|
@ -126,14 +127,14 @@ def download_revisions(rev_ids: Collection[int]) -> None:
|
|||
if rev["id"] in new_rev_ids:
|
||||
new_rev_ids.remove(rev["id"])
|
||||
|
||||
print(f"Loaded {old_rev_count} revisions.")
|
||||
logger.info("Loaded %d revisions.", old_rev_count)
|
||||
|
||||
new_rev_ids_list = sorted(list(new_rev_ids))
|
||||
rev_ids_groups = (
|
||||
new_rev_ids_list[i : i + 100] for i in range(0, len(new_rev_ids_list), 100)
|
||||
)
|
||||
|
||||
print(f"{len(new_rev_ids_list)} revisions left to download")
|
||||
logger.info("%d revisions left to download", len(new_rev_ids_list))
|
||||
|
||||
with tqdm(total=len(new_rev_ids)) as progress_bar:
|
||||
for rev_ids_group in rev_ids_groups:
|
||||
|
|
|
|||
|
|
@ -235,15 +235,15 @@ class BaseSimilarity(abc.ABC):
|
|||
|
||||
apk.append(score / min(len(duplicates[bug["id"]]), 10))
|
||||
|
||||
print(f"Recall @ 1: {recall_rate_1/total_r * 100}%")
|
||||
print(f"Recall @ 5: {recall_rate_5/total_r * 100}%")
|
||||
print(f"Recall @ 10: {recall_rate_10/total_r * 100}%")
|
||||
print(f"Precision @ 1: {precision_rate_1/queries * 100}%")
|
||||
print(f"Precision @ 5: {precision_rate_5/queries * 100}%")
|
||||
print(f"Precision @ 10: {precision_rate_10/queries * 100}%")
|
||||
print(f"Recall: {hits_r/total_r * 100}%")
|
||||
print(f"Precision: {hits_p/total_p * 100}%")
|
||||
print(f"MAP@k : {np.mean(apk) * 100}%")
|
||||
logger.info("Recall @ 1: %d%", recall_rate_1 / total_r * 100)
|
||||
logger.info("Recall @ 5: %d%", recall_rate_5 / total_r * 100)
|
||||
logger.info("Recall @ 10: %d%", recall_rate_10 / total_r * 100)
|
||||
logger.info("Precision @ 1: %d%", precision_rate_1 / queries * 100)
|
||||
logger.info("Precision @ 5: %d%", precision_rate_5 / queries * 100)
|
||||
logger.info("Precision @ 10: %d%", precision_rate_10 / queries * 100)
|
||||
logger.info("Recall: %d%", hits_r / total_r * 100)
|
||||
logger.info("Precision: %d%", hits_p / total_p * 100)
|
||||
logger.info("MAP@k : %d%", np.mean(apk) * 100)
|
||||
|
||||
@abc.abstractmethod
|
||||
def get_distance(self, query1, query2):
|
||||
|
|
@ -407,7 +407,7 @@ class Word2VecWmdSimilarity(Word2VecSimilarityBase):
|
|||
def wmdistance(self, document1, document2, all_distances, distance_metric="cosine"):
|
||||
model = self.w2vmodel
|
||||
if len(document1) == 0 or len(document2) == 0:
|
||||
print(
|
||||
logger.warning(
|
||||
"At least one of the documents had no words that were in the vocabulary. Aborting (returning inf)."
|
||||
)
|
||||
return float("inf")
|
||||
|
|
@ -434,7 +434,9 @@ class Word2VecWmdSimilarity(Word2VecSimilarityBase):
|
|||
distance_matrix[i, j] = all_distances[model.wv.vocab[t2].index, i]
|
||||
|
||||
if np.sum(distance_matrix) == 0.0:
|
||||
print("The distance matrix is all zeros. Aborting (returning inf).")
|
||||
logger.warning(
|
||||
"The distance matrix is all zeros. Aborting (returning inf)."
|
||||
)
|
||||
return float("inf")
|
||||
|
||||
def nbow(document):
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ from tqdm import tqdm
|
|||
from bugbug import db, repository
|
||||
from bugbug.utils import ExpQueue, LMDBDict
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
Revision = NewType("Revision", str)
|
||||
|
|
@ -281,8 +282,9 @@ def get_push_data(
|
|||
)
|
||||
)
|
||||
|
||||
print(
|
||||
f"{manifest_combinations} possible combinations of manifests on configurations"
|
||||
logger.info(
|
||||
"%d possible combinations of manifests on configurations",
|
||||
manifest_combinations,
|
||||
)
|
||||
|
||||
return push_data_iter, push_data_count, all_runnables
|
||||
|
|
|
|||
|
|
@ -5,9 +5,13 @@
|
|||
|
||||
import os
|
||||
import time
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import requests
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
BUGBUG_HTTP_SERVER = os.environ.get("BUGBUG_HTTP_SERVER", "http://localhost:8000/")
|
||||
|
||||
|
||||
|
|
@ -30,7 +34,7 @@ def integration_test_single():
|
|||
if not response.ok:
|
||||
raise Exception(f"Couldn't get an answer in {timeout} seconds: {response_json}")
|
||||
|
||||
print("Response for bug 1376406", response_json)
|
||||
logger.info("Response for bug 1376406 %s", response_json)
|
||||
assert response_json["class"] is not None
|
||||
|
||||
|
||||
|
|
@ -55,10 +59,10 @@ def integration_test_batch():
|
|||
raise Exception(f"Couldn't get an answer in {timeout} seconds: {response_json}")
|
||||
|
||||
response_1376544 = response_json["bugs"]["1376544"]
|
||||
print("Response for bug 1376544", response_1376544)
|
||||
logger.info("Response for bug 1376544 %s", response_1376544)
|
||||
assert response_1376544["class"] is not None
|
||||
response_1376412 = response_json["bugs"]["1376412"]
|
||||
print("Response for bug 1376412", response_1376412)
|
||||
logger.info("Response for bug 1376412 %s", response_1376412)
|
||||
assert response_1376412["class"] is not None
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -21,13 +21,16 @@ This script triggers the data pipeline for the bugbug project
|
|||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import jsone
|
||||
import requests.packages.urllib3
|
||||
import taskcluster
|
||||
import yaml
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
requests.packages.urllib3.disable_warnings()
|
||||
|
||||
TASKCLUSTER_DEFAULT_URL = "https://community-tc.services.mozilla.com"
|
||||
|
|
@ -128,9 +131,11 @@ def main():
|
|||
for task_id, task_payload in tasks:
|
||||
queue.createTask(task_id, task_payload)
|
||||
|
||||
print(f"https://community-tc.services.mozilla.com/tasks/groups/{task_group_id}")
|
||||
except taskcluster.exceptions.TaskclusterAuthFailure as e:
|
||||
print(f"TaskclusterAuthFailure: {e.body}", file=sys.stderr)
|
||||
logger.info(
|
||||
"https://community-tc.services.mozilla.com/tasks/groups/%s", task_group_id
|
||||
)
|
||||
except taskcluster.exceptions.TaskclusterAuthFailure:
|
||||
logger.exception("Failed to authenticate with Taskcluster")
|
||||
raise
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,12 +4,16 @@
|
|||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
import argparse
|
||||
import json
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
from mozci.push import Push
|
||||
from tqdm import tqdm
|
||||
|
||||
from bugbug import db, repository
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def go() -> None:
|
||||
assert db.download(repository.COMMITS_DB)
|
||||
|
|
@ -49,10 +53,10 @@ def go() -> None:
|
|||
"group": group_regressions,
|
||||
}
|
||||
|
||||
print(f"Likely labels for backouts: {likely_label_count}")
|
||||
print(f"Likely groups for backouts: {likely_group_count}")
|
||||
print(f"Possible labels for backouts: {possible_label_count}")
|
||||
print(f"Possible groups for backouts: {possible_group_count}")
|
||||
logger.info("Likely labels for backouts: %d", likely_label_count)
|
||||
logger.info("Likely groups for backouts: %d", likely_group_count)
|
||||
logger.info("Possible labels for backouts: %d", possible_label_count)
|
||||
logger.info("Possible groups for backouts: %d", possible_group_count)
|
||||
|
||||
backedout_regressions = {}
|
||||
|
||||
|
|
|
|||
|
|
@ -4,12 +4,16 @@ import argparse
|
|||
import csv
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import numpy as np
|
||||
|
||||
from bugbug import bugzilla
|
||||
from bugbug.models import get_model_class
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def generate_sheet(model_name: str, token: str, days: int, threshold: float) -> None:
|
||||
model_file_name = f"{model_name}model"
|
||||
|
|
@ -25,7 +29,7 @@ def generate_sheet(model_name: str, token: str, days: int, threshold: float) ->
|
|||
bug_ids = bugzilla.get_ids_between(datetime.utcnow() - timedelta(days))
|
||||
bugs = bugzilla.get(bug_ids)
|
||||
|
||||
print(f"Classifying {len(bugs)} bugs...")
|
||||
logger.info("Classifying %d bugs...", len(bugs))
|
||||
|
||||
rows = [["Bug", f"{model_name}(model)", model_name, "Title"]]
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,10 @@ import json
|
|||
import os
|
||||
import sys
|
||||
from datetime import date, datetime, timedelta
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
# Inject project path
|
||||
sys.path.append("../")
|
||||
|
|
@ -61,16 +65,16 @@ def run_untriaged(untriaged_bugs):
|
|||
bug["product"], bug["component"]
|
||||
)
|
||||
if not expected_component:
|
||||
print("Skipping bug: {}".format(bug["id"]))
|
||||
logger.info("Skipping bug: %s", bug["id"])
|
||||
continue
|
||||
|
||||
if classifiable:
|
||||
print("Classifying bug with ID: {}".format(bug["id"]))
|
||||
logger.info("Classifying bug with ID: %s", bug["id"])
|
||||
classification = model.classify(bug)[0]
|
||||
print("Classified bug as: {}".format(classification))
|
||||
logger.info("Classified bug as: %s", classification)
|
||||
|
||||
else:
|
||||
print("Not classifiable bug: {}".format(bug["id"]))
|
||||
logger.info("Not classifiable bug: %s", bug["id"])
|
||||
|
||||
correct_prediction = expected_component == classification
|
||||
rows.append(
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ def classify_issues(
|
|||
issues = github.get_issues()
|
||||
|
||||
for issue in issues:
|
||||
print(f'{issue["url"]} - {issue["title"]} ')
|
||||
logger.info("%s - %s ", issue["url"], issue["title"])
|
||||
|
||||
if model.calculate_importance:
|
||||
probas, importance = model.classify(
|
||||
|
|
@ -67,7 +67,7 @@ def classify_issues(
|
|||
pred_class = model.le.inverse_transform([pred_index])[0]
|
||||
else:
|
||||
pred_class = "Positive" if pred_index == 1 else "Negative"
|
||||
print(f"{pred_class} {probability}")
|
||||
logger.info("%s %s", pred_class, probability)
|
||||
input()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,13 +4,14 @@
|
|||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
import argparse
|
||||
from logging import getLogger
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import dateutil.parser
|
||||
|
||||
from bugbug import bugzilla
|
||||
from bugbug.utils import get_secret
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
|
|
@ -44,7 +45,7 @@ def main():
|
|||
"If you want to include security bugs too, please set the BUGBUG_BUGZILLA_TOKEN environment variable to your Bugzilla API key."
|
||||
)
|
||||
|
||||
print(
|
||||
logger.info(
|
||||
round(
|
||||
bugzilla.calculate_maintenance_effectiveness_indicator(
|
||||
args.team,
|
||||
|
|
|
|||
|
|
@ -3,19 +3,24 @@
|
|||
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
|
||||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
from bugbug import db, test_scheduling
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def count(is_first_task, is_second_task):
|
||||
assert db.download(test_scheduling.PUSH_DATA_LABEL_DB)
|
||||
|
||||
push_data = list(db.read(test_scheduling.PUSH_DATA_LABEL_DB))
|
||||
|
||||
print(f"Analyzing {len(push_data)} pushes...")
|
||||
logger.info("Analyzing %d pushes...", len(push_data))
|
||||
|
||||
all_tasks = set(task for _, _, push_tasks, _, _ in push_data for task in push_tasks)
|
||||
|
||||
print(f"Considering {len(all_tasks)} tasks...")
|
||||
logger.info("Considering %d tasks...", len(all_tasks))
|
||||
|
||||
count_runs = 0
|
||||
count_any_of_the_two = 0
|
||||
|
|
@ -96,8 +101,12 @@ def main():
|
|||
count_first_but_not_second,
|
||||
count_second_but_not_first,
|
||||
) = count(is_first_task, is_second_task)
|
||||
print(
|
||||
f"Out of {count_runs} runs, any of the two failed {count_any_of_the_two} times. The first exclusively failed {count_first_but_not_second} times, the second exclusively failed {count_second_but_not_first} times."
|
||||
logger.info(
|
||||
"Out of %d runs, any of the two failed %d times. The first exclusively failed %d times, the second exclusively failed %d times.",
|
||||
count_runs,
|
||||
count_any_of_the_two,
|
||||
count_first_but_not_second,
|
||||
count_second_but_not_first,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ from tqdm import tqdm
|
|||
|
||||
from bugbug import db, test_scheduling, utils
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
|
|
@ -298,19 +299,32 @@ def print_uncaught(
|
|||
|
||||
if len(caught_by_scheduler[scheduler1]) == 0:
|
||||
if scheduler2 is not None and scheduler2 not in caught_by_scheduler:
|
||||
print(
|
||||
f"{scheduler1} didn't catch any of the {len(regressions)} regressions on {rev}"
|
||||
logger.info(
|
||||
"%s didn't catch any of the %d regressions on %s",
|
||||
scheduler1,
|
||||
len(regressions),
|
||||
rev,
|
||||
)
|
||||
elif scheduler2 is not None and len(caught_by_scheduler[scheduler2]) == 0:
|
||||
print(
|
||||
f"{scheduler1} and {scheduler2} didn't catch any of the {len(regressions)} regressions on {rev}"
|
||||
logger.info(
|
||||
"%s and %s didn't catch any of the %d regressions on %s",
|
||||
scheduler1,
|
||||
scheduler2,
|
||||
len(regressions),
|
||||
rev,
|
||||
)
|
||||
else:
|
||||
print(
|
||||
f"{scheduler1} didn't catch any of the {len(regressions)} regressions on {rev}, while {scheduler2} did"
|
||||
logger.info(
|
||||
"%s didn't catch any of the %d regressions on %s, while %s did",
|
||||
scheduler1,
|
||||
len(regressions),
|
||||
rev,
|
||||
scheduler2,
|
||||
)
|
||||
print(f"Regressions: {regressions}")
|
||||
print(f"Scheduled by {scheduler1}: {scheduled_by_scheduler[scheduler1]}")
|
||||
logger.info("Regressions: %s", regressions)
|
||||
logger.info(
|
||||
"Scheduled by %s: %s", scheduler1, scheduled_by_scheduler[scheduler1]
|
||||
)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
|
|
|
|||
|
|
@ -4,10 +4,14 @@
|
|||
# You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
import importlib
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
from bugbug import model
|
||||
from bugbug.models import MODELS, get_model_class
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def test_import_all_models():
|
||||
"""Try loading all defined models to ensure that their full qualified
|
||||
|
|
@ -18,7 +22,7 @@ def test_import_all_models():
|
|||
if model_name == "component_nn" and not importlib.util.find_spec("tensorflow"):
|
||||
continue
|
||||
|
||||
print("Try loading model", model_name)
|
||||
logger.info("Try loading model %s", model_name)
|
||||
get_model_class(model_name)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import pickle
|
|||
import shutil
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from logging import INFO, basicConfig, getLogger
|
||||
|
||||
import hglib
|
||||
import pytest
|
||||
|
|
@ -19,6 +20,9 @@ from dateutil.relativedelta import relativedelta
|
|||
|
||||
from bugbug import commit_features, repository, rust_code_analysis_server
|
||||
|
||||
basicConfig(level=INFO)
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def fake_hg_repo(tmpdir):
|
||||
|
|
@ -2399,7 +2403,7 @@ void main() {
|
|||
|
||||
patch_data = rs_parsepatch.get_lines(patch)
|
||||
|
||||
print(patch_data)
|
||||
logger.info(patch_data)
|
||||
|
||||
assert len(patch_data) == 1
|
||||
|
||||
|
|
@ -2650,7 +2654,7 @@ void main() {
|
|||
|
||||
patch_data = rs_parsepatch.get_lines(patch)
|
||||
|
||||
print(patch_data)
|
||||
logger.info(patch_data)
|
||||
|
||||
assert len(patch_data) == 1
|
||||
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче