Iteratively increase sample size for good/bad classification.

scripts/slave/recipe_modules/auto_bisect/bisector.py

 # Copyright 2015 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 json
 import re
+import time
 
 from . import bisect_results
 from . import depot_config
 from . import revision_state
 
 _DEPS_SHA_PATCH = """
 diff --git DEPS.sha DEPS.sha
 new file mode 100644
 --- /dev/null
 +++ DEPS.sha
@@ skipping 18 matching lines @@
     'LO_INIT_CONF',  # Bisect aborted early for lack of confidence.
     'MISSING_METRIC',  # The metric was not found in the test text/json output.
     'LO_FINAL_CONF',  # The bisect completed without a culprit.
 )
 
 # When we look for the next revision to build, we search nearby revisions
 # looking for a revision that's already been archived. Since we don't want
 # to move *too* far from the original revision, we'll cap the search at 25%.
 DEFAULT_SEARCH_RANGE_PERCENTAGE = 0.25
 
+# How long to re-test the initial good-bad range for until significant
+# difference is established.
+REGRESSION_CHECK_TIMEOUT = 6 * 60 * 60

[dtu, 2016/02/05 00:18:01]

Is that six hours? Do we really have that much time?

+
+# Significance level to use for determining difference between revisions via
+# hypothesis testing.
+SIGNIFICANCE_LEVEL = 0.05
+
 
 class Bisector(object):
   """This class abstracts an ongoing bisect (or n-sect) job."""
 
   def __init__(self, api, bisect_config, revision_class, init_revisions=True):
     """Initializes the state of a new bisect job from a dictionary.
 
     Note that the initial good_rev and bad_rev MUST resolve to a commit position
     in the chromium repo.
     """
@@ skipping 15 matching lines @@
     self.test_type = bisect_config.get('test_type', 'perf')
     self.improvement_direction = int(bisect_config.get(
         'improvement_direction', 0)) or None
     # Required initial confidence might be explicitly set to None,
     # which means that no initial confidence check should be done.
     if ('required_initial_confidence' in bisect_config and
         bisect_config['required_initial_confidence'] is None):
       self.required_initial_confidence = None  # pragma: no cover
     else:
       self.required_initial_confidence = float(bisect_config.get(
           'required_initial_confidence', 80))

[dtu, 2016/02/05 00:18:02]

Should we rethink what this value is? Also make it a constant?

 
     self.warnings = []
 
     # Status flags
     self.initial_confidence = None
     self.failed_initial_confidence = False
     self.failed = False
     self.failed_direction = False
     self.lkgr = None  # Last known good revision
     self.fkbr = None  # First known bad revision
@@ skipping 16 matching lines @@
       self.good_rev.good = True
       self.good_rev.read_deps(self.get_perf_tester_name())
       api.m.step.active_result.presentation.logs['Debug Good Revision DEPS'] = [
           '%s: %s' % (key, value) for key, value in
           self.good_rev.deps.iteritems()]
       self.good_rev.deps = {}
       self.lkgr = self.good_rev
     if init_revisions:
       self._expand_chromium_revision_range()
 
+  def significantly_different(
+      self, list_a, list_b,
+      significance_level=SIGNIFICANCE_LEVEL): # pragma: no cover
+    """Uses an external script to run hypothesis testing with scipy.
+
+    The reason why we need an external script is that scipy is not available to
+    the default python installed in all platforms. We instead rely on an
+    anaconda environment to provide those packages.
+
+    Args:
+      list_a, list_b: Two lists representing samples to be compared.
+      significance_level: Self-describing. As a decimal fraction.
+
+    Returns:
+      A boolean indicating whether the null hypothesis ~(that the lists are
+      samples from the same population) can be rejected at the specified
+      significance level.
+    """
+    step_result = self.api.m.python(
+        'Checking sample difference',
+        self.api.resource('significantly_different.py'),
+        [json.dumps(list_a), json.dumps(list_b), str(significance_level)],
+        stdout=self.api.m.json.output())
+    results = step_result.stdout
+    if results is None:
+      assert self.dummy_builds
+      return True
+    significantly_different = results['significantly_different']
+    step_result.presentation.logs[str(significantly_different)] = [
+        'See json.output for details']
+    return significantly_different
+
   def config_step(self):
     """Yields a simple echo step that outputs the bisect config."""
     api = self.api
     # bisect_config may come as a FrozenDict (which is not serializable).
     bisect_config = dict(self.bisect_config)
 
     def fix_windows_backslashes(s):
       backslash_regex = re.compile(r'(?<!\\)\\(?!\\)')
       return backslash_regex.sub(r'\\', s)
 
@@ skipping 278 matching lines @@
     self.failed_direction = True
     self.warnings.append('The initial regression range for return code '
                          'appears to show NO sign of a regression.')
 
   def _set_failed_direction_results(self):  # pragma: no cover
     self.failed_direction = True
     self.warnings.append('The initial regression range appears to represent '
                          'an improvement rather than a regression, given the '
                          'expected direction of improvement.')
 
-  def check_initial_confidence(self):
+  def check_initial_confidence(self):  # pragma: no cover
     """Checks that the initial range presents a clear enough regression.
 
     We calculate the confidence of the results of the given 'good'
     and 'bad' revisions and compare it against the required confidence
     set for the bisector.
 
     Note that when a dummy regression confidence value has been set, that
     is used instead.
     """
     if self.test_type != 'perf':
       return True
 
     if self.required_initial_confidence is None:
       return True  # pragma: no cover
 
+    # TODO(robertocn): Remove all uses of "confidence".
     if self.dummy_initial_confidence is not None:
       self.initial_confidence = float(
           self.dummy_initial_confidence)
+      if (float(self.initial_confidence) <
+          float(self.required_initial_confidence)):
+        self._set_insufficient_confidence_warning()
+        return False
+      return True
 
-    else:  # pragma: no cover
-      if len(self.good_rev.values) < 5 or len(self.bad_rev.values) < 5:
-        # If there are too few values, the confidence score is not a good way to
-        # determine whether the regression is reproducible.
-        # TODO(robertocn): Investigate a straightforward approach to deal with
-        # these cases. Such as the mean of one group lying within the range of
-        # the other.
-        return True
-      self.initial_confidence = (
-          self.api.m.math_utils.confidence_score(
-              self.good_rev.values,
-              self.bad_rev.values))
-    if (self.initial_confidence <
-        self.required_initial_confidence):  # pragma: no cover
-      self._set_insufficient_confidence_warning(self.initial_confidence)
-      return False
-    return True
+    if self.dummy_builds:
+      dummy_result = self.good_rev.values != self.bad_rev.values
+      if not dummy_result:
+        self._set_insufficient_confidence_warning()
+      return dummy_result

[dtu, 2016/02/05 00:18:01]

Seems like there's a bunch of technical debt related to this dummy test data.
It's written in a cross-cutting way.

+
+    expiration_time = time.time() + REGRESSION_CHECK_TIMEOUT
+    while time.time() < expiration_time:
+      if len(self.good_rev.values) >= 5 and len(self.bad_rev.values) >= 5:
+        if self.significantly_different(self.good_rev.values,
+                                        self.bad_rev.values):
+          return True
+      min(self.good_rev, self.bad_rev, key=lambda x: len(x.values)).retest()
+    self._set_insufficient_confidence_warning()
+    return False
+
 
   def get_exception(self):
     raise NotImplementedError()  # pragma: no cover
     # TODO: should return an exception with the details of the failure.
 
   def _set_insufficient_confidence_warning(
-      self, actual_confidence):  # pragma: no cover
+      self):  # pragma: no cover
     """Adds a warning about the lack of initial regression confidence."""
     self.failed_initial_confidence = True
     self.surface_result('LO_INIT_CONF')
     self.warnings.append(
-        ('Bisect failed to reproduce the regression with enough confidence. '
-         'Needed {:.2f}%, got {:.2f}%.').format(
-             self.required_initial_confidence, actual_confidence))
+        'Bisect failed to reproduce the regression with enough confidence.')
 
   def _results_debug_message(self):
     """Returns a string with values used to debug a bisect result."""
     result = 'bisector.lkgr: %r\n' % self.lkgr
     result += 'bisector.fkbr: %r\n\n' % self.fkbr
     result += self._revision_value_table()
     if (self.lkgr and self.lkgr.values and self.fkbr and self.fkbr.values):
       result += '\n' + self._t_test_results()
     return result
 
@@ skipping 334 matching lines @@
 
   def surface_result(self, result_string):
     assert result_string in VALID_RESULT_CODES
     prefix = 'B4T_'  # To avoid collision. Stands for bisect (abbr. `a la i18n).
     result_code = prefix + result_string
     assert len(result_code) <= 20
     if result_code not in self.result_codes:
       self.result_codes.add(result_code)
       properties = self.api.m.step.active_result.presentation.properties
       properties['extra_result_code'] = sorted(self.result_codes)