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# Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import math
import os
import bisect_utils
import math_utils
import source_control
import ttest
from bisect_state import RevisionState
class BisectResults(object):
"""Contains results of the completed bisect.
Properties:
error: Error message if the bisect failed.
If the error is None, the following properties are present:
warnings: List of warnings from the bisect run.
state: BisectState object from which these results were generated.
first_working_revision: First good revision.
last_broken_revision: Last bad revision.
If both of above revisions are not None, the follow properties are present:
culprit_revisions: A list of revisions, which contain the bad change
introducing the failure.
other_regressions: A list of tuples representing other regressions, which
may have occurred.
regression_size: For performance bisects, this is a relative change of
the mean metric value. For other bisects this field always contains
'zero-to-nonzero'.
regression_std_err: For performance bisects, it is a pooled standard error
for groups of good and bad runs. Not used for other bisects.
confidence: For performance bisects, it is a confidence that the good and
bad runs are distinct groups. Not used for non-performance bisects.
"""
def __init__(self, bisect_state=None, depot_registry=None, opts=None,
runtime_warnings=None, error=None, abort_reason=None):
"""Computes final bisect results after a bisect run is complete.
This constructor should be called in one of the following ways:
BisectResults(state, depot_registry, opts, runtime_warnings)
BisectResults(error=error)
First option creates an object representing successful bisect results, while
second option creates an error result.
Args:
bisect_state: BisectState object representing latest bisect state.
depot_registry: DepotDirectoryRegistry object with information on each
repository in the bisect_state.
opts: Options passed to the bisect run.
runtime_warnings: A list of warnings from the bisect run.
error: Error message. When error is not None, other arguments are ignored.
"""
self.error = error
self.abort_reason = abort_reason
if error is not None or abort_reason is not None:
return
assert (bisect_state is not None and depot_registry is not None and
opts is not None and runtime_warnings is not None), (
'Incorrect use of the BisectResults constructor. '
'When error is None, all other arguments are required.')
self.state = bisect_state
rev_states = bisect_state.GetRevisionStates()
first_working_rev, last_broken_rev = self.FindBreakingRevRange(rev_states)
self.first_working_revision = first_working_rev
self.last_broken_revision = last_broken_rev
self.warnings = runtime_warnings
self.retest_results_tot = None
self.retest_results_reverted = None
if first_working_rev is not None and last_broken_rev is not None:
statistics = self._ComputeRegressionStatistics(
rev_states, first_working_rev, last_broken_rev)
self.regression_size = statistics['regression_size']
self.regression_std_err = statistics['regression_std_err']
self.confidence = statistics['confidence']
self.culprit_revisions = self._FindCulpritRevisions(
rev_states, depot_registry, first_working_rev, last_broken_rev)
self.other_regressions = self._FindOtherRegressions(
rev_states, statistics['bad_greater_than_good'])
self.warnings += self._GetResultBasedWarnings(
self.culprit_revisions, opts, self.confidence)
elif first_working_rev is not None:
# Setting these attributes so that bisect printer does not break when the
# regression cannot be reproduced (no broken revision was found)
self.regression_size = 0
self.regression_std_err = 0
self.confidence = 0
self.culprit_revisions = []
self.other_regressions = []
def AddRetestResults(self, results_tot, results_reverted):
if not results_tot or not results_reverted:
self.warnings.append(
'Failed to re-test reverted culprit CL against ToT.')
return
confidence_params = (results_reverted[0]['values'],
results_tot[0]['values'])
confidence = BisectResults.ConfidenceScore(*confidence_params)
self.retest_results_tot = RevisionState('ToT', 'n/a', 0)
self.retest_results_tot.value = results_tot[0]
self.retest_results_reverted = RevisionState('Reverted', 'n/a', 0)
self.retest_results_reverted.value = results_reverted[0]
if confidence <= bisect_utils.HIGH_CONFIDENCE:
self.warnings.append(
'Confidence of re-test with reverted CL is not high.'
' Check that the regression hasn\'t already recovered. '
' There\'s still a chance this is a regression, as performance of'
' local builds may not match official builds.')
@staticmethod
def _GetResultBasedWarnings(culprit_revisions, opts, confidence):
warnings = []
if len(culprit_revisions) > 1:
warnings.append('Due to build errors, regression range could '
'not be narrowed down to a single commit.')
if opts.repeat_test_count == 1:
warnings.append('Tests were only set to run once. This may '
'be insufficient to get meaningful results.')
if 0 < confidence < bisect_utils.HIGH_CONFIDENCE:
warnings.append('Confidence is not high. Try bisecting again '
'with increased repeat_count, larger range, or '
'on another metric.')
if not confidence:
warnings.append('Confidence score is 0%. Try bisecting again on '
'another platform or another metric.')
return warnings
@staticmethod
def ConfidenceScore(sample1, sample2,
accept_single_bad_or_good=False):
"""Calculates a confidence score.
This score is a percentage which represents our degree of confidence in the
proposition that the good results and bad results are distinct groups, and
their differences aren't due to chance alone.
Args:
sample1: A flat list of "good" result numbers.
sample2: A flat list of "bad" result numbers.
accept_single_bad_or_good: If True, computes confidence even if there is
just one bad or good revision, otherwise single good or bad revision
always returns 0.0 confidence. This flag will probably get away when
we will implement expanding the bisect range by one more revision for
such case.
Returns:
A number in the range [0, 100].
"""
# If there's only one item in either list, this means only one revision was
# classified good or bad; this isn't good enough evidence to make a
# decision. If an empty list was passed, that also implies zero confidence.
if not accept_single_bad_or_good:
if len(sample1) <= 1 or len(sample2) <= 1:
return 0.0
# If there were only empty lists in either of the lists (this is unexpected
# and normally shouldn't happen), then we also want to return 0.
if not sample1 or not sample2:
return 0.0
# The p-value is approximately the probability of obtaining the given set
# of good and bad values just by chance.
_, _, p_value = ttest.WelchsTTest(sample1, sample2)
return 100.0 * (1.0 - p_value)
@classmethod
def _FindOtherRegressions(cls, revision_states, bad_greater_than_good):
"""Compiles a list of other possible regressions from the revision data.
Args:
revision_states: Sorted list of RevisionState objects.
bad_greater_than_good: Whether the result value at the "bad" revision is
numerically greater than the result value at the "good" revision.
Returns:
A list of [current_rev, previous_rev, confidence] for other places where
there may have been a regression.
"""
other_regressions = []
previous_values = []
prev_state = None
for revision_state in revision_states:
if revision_state.value:
current_values = revision_state.value['values']
if previous_values:
confidence_params = (sum(previous_values, []),
sum([current_values], []))
confidence = cls.ConfidenceScore(*confidence_params,
accept_single_bad_or_good=True)
mean_of_prev_runs = math_utils.Mean(sum(previous_values, []))
mean_of_current_runs = math_utils.Mean(current_values)
# Check that the potential regression is in the same direction as
# the overall regression. If the mean of the previous runs < the
# mean of the current runs, this local regression is in same
# direction.
prev_greater_than_current = mean_of_prev_runs > mean_of_current_runs
if bad_greater_than_good:
is_same_direction = prev_greater_than_current
else:
is_same_direction = not prev_greater_than_current
# Only report potential regressions with high confidence.
if is_same_direction and confidence > 50:
other_regressions.append([revision_state, prev_state, confidence])
previous_values.append(current_values)
prev_state = revision_state
return other_regressions
@staticmethod
def FindBreakingRevRange(revision_states):
"""Finds the last known good and first known bad revisions.
Note that since revision_states is expected to be in reverse chronological
order, the last known good revision is the first revision in the list that
has the passed property set to 1, therefore the name
`first_working_revision`. The inverse applies to `last_broken_revision`.
Args:
revision_states: A list of RevisionState instances.
Returns:
A tuple containing the two revision states at the border. (Last
known good and first known bad.)
"""
first_working_revision = None
last_broken_revision = None
for revision_state in revision_states:
if revision_state.passed == 1 and not first_working_revision:
first_working_revision = revision_state
if not revision_state.passed:
last_broken_revision = revision_state
return first_working_revision, last_broken_revision
@staticmethod
def _FindCulpritRevisions(revision_states, depot_registry, first_working_rev,
last_broken_rev):
cwd = os.getcwd()
culprit_revisions = []
for i in xrange(last_broken_rev.index, first_working_rev.index):
depot_registry.ChangeToDepotDir(revision_states[i].depot)
info = source_control.QueryRevisionInfo(revision_states[i].revision)
culprit_revisions.append((revision_states[i].revision, info,
revision_states[i].depot))
os.chdir(cwd)
return culprit_revisions
@classmethod
def _ComputeRegressionStatistics(cls, rev_states, first_working_rev,
last_broken_rev):
# TODO(sergiyb): We assume that value has "values" key, which may not be
# the case for failure-bisects, where there is a single value only.
broken_means = [state.value['values']
for state in rev_states[:last_broken_rev.index+1]
if state.value]
working_means = [state.value['values']
for state in rev_states[first_working_rev.index:]
if state.value]
# Flatten the lists to calculate mean of all values.
working_mean = sum(working_means, [])
broken_mean = sum(broken_means, [])
# Calculate the approximate size of the regression
mean_of_bad_runs = math_utils.Mean(broken_mean)
mean_of_good_runs = math_utils.Mean(working_mean)
regression_size = 100 * math_utils.RelativeChange(mean_of_good_runs,
mean_of_bad_runs)
if math.isnan(regression_size):
regression_size = 'zero-to-nonzero'
regression_std_err = math.fabs(math_utils.PooledStandardError(
[working_mean, broken_mean]) /
max(0.0001, min(mean_of_good_runs, mean_of_bad_runs))) * 100.0
# Give a "confidence" in the bisect. Currently, we consider the values of
# only the revisions at the breaking range (last known good and first known
# bad) see the note in the docstring for FindBreakingRange.
confidence_params = (
sum([first_working_rev.value['values']], []),
sum([last_broken_rev.value['values']], [])
)
confidence = cls.ConfidenceScore(*confidence_params)
bad_greater_than_good = mean_of_bad_runs > mean_of_good_runs
return {'regression_size': regression_size,
'regression_std_err': regression_std_err,
'confidence': confidence,
'bad_greater_than_good': bad_greater_than_good}