Merge branch 'aosp/mirror-chromium-master' into update_utils am: 4307f4735e am: 18caef1edd am: 6c551e0d52 am: ddfea1f7e7android-wear-9.0.0_r9android-wear-9.0.0_r8android-wear-9.0.0_r7android-wear-9.0.0_r6android-wear-9.0.0_r5android-wear-9.0.0_r4android-wear-9.0.0_r34android-wear-9.0.0_r33android-wear-9.0.0_r32android-wear-9.0.0_r31android-wear-9.0.0_r30android-wear-9.0.0_r3android-wear-9.0.0_r29android-wear-9.0.0_r28android-wear-9.0.0_r27android-wear-9.0.0_r26android-wear-9.0.0_r25android-wear-9.0.0_r24android-wear-9.0.0_r23android-wear-9.0.0_r22android-wear-9.0.0_r21android-wear-9.0.0_r20android-wear-9.0.0_r2android-wear-9.0.0_r19android-wear-9.0.0_r18android-wear-9.0.0_r17android-wear-9.0.0_r16android-wear-9.0.0_r15android-wear-9.0.0_r14android-wear-9.0.0_r13android-wear-9.0.0_r12android-wear-9.0.0_r11android-wear-9.0.0_r10android-wear-9.0.0_r1android-vts-9.0_r9android-vts-9.0_r8android-vts-9.0_r7android-vts-9.0_r6android-vts-9.0_r5android-vts-9.0_r4android-vts-9.0_r19android-vts-9.0_r18android-vts-9.0_r17android-vts-9.0_r16android-vts-9.0_r15android-vts-9.0_r14android-vts-9.0_r13android-vts-9.0_r12android-vts-9.0_r11android-vts-9.0_r10android-security-9.0.0_r76android-security-9.0.0_r75android-security-9.0.0_r74android-security-9.0.0_r73android-security-9.0.0_r72android-security-9.0.0_r71android-security-9.0.0_r70android-security-9.0.0_r69android-security-9.0.0_r68android-security-9.0.0_r67android-security-9.0.0_r66android-security-9.0.0_r65android-security-9.0.0_r64android-security-9.0.0_r63android-security-9.0.0_r62android-o-mr1-iot-release-1.0.4android-o-mr1-iot-release-1.0.3android-cts-9.0_r9android-cts-9.0_r8android-cts-9.0_r7android-cts-9.0_r6android-cts-9.0_r5android-cts-9.0_r4android-cts-9.0_r3android-cts-9.0_r20android-cts-9.0_r2android-cts-9.0_r19android-cts-9.0_r18android-cts-9.0_r17android-cts-9.0_r16android-cts-9.0_r15android-cts-9.0_r14android-cts-9.0_r13android-cts-9.0_r12android-cts-9.0_r11android-cts-9.0_r10android-cts-9.0_r1android-9.0.0_r9android-9.0.0_r8android-9.0.0_r7android-9.0.0_r61android-9.0.0_r60android-9.0.0_r6android-9.0.0_r59android-9.0.0_r58android-9.0.0_r57android-9.0.0_r56android-9.0.0_r55android-9.0.0_r54android-9.0.0_r53android-9.0.0_r52android-9.0.0_r51android-9.0.0_r50android-9.0.0_r5android-9.0.0_r49android-9.0.0_r48android-9.0.0_r47android-9.0.0_r46android-9.0.0_r45android-9.0.0_r44android-9.0.0_r43android-9.0.0_r42android-9.0.0_r41android-9.0.0_r40android-9.0.0_r39android-9.0.0_r38android-9.0.0_r37android-9.0.0_r36android-9.0.0_r35android-9.0.0_r34android-9.0.0_r33android-9.0.0_r32android-9.0.0_r31android-9.0.0_r30android-9.0.0_r3android-9.0.0_r22android-9.0.0_r21android-9.0.0_r20android-9.0.0_r2android-9.0.0_r19android-9.0.0_r18android-9.0.0_r17android-9.0.0_r16android-9.0.0_r12android-9.0.0_r11android-9.0.0_r10android-9.0.0_r1security-pi-releasepie-vts-releasepie-security-releasepie-s2-releasepie-release-2pie-releasepie-r2-s2-releasepie-r2-s1-releasepie-r2-releasepie-qpr3-s1-releasepie-qpr3-releasepie-qpr3-b-releasepie-qpr2-releasepie-qpr1-s3-releasepie-qpr1-s2-releasepie-qpr1-s1-releasepie-qpr1-releasepie-platform-releasepie-gsipie-dr1-releasepie-dr1-devpie-devpie-cuttlefish-testingpie-cts-releasepie-b4s4-releasepie-b4s4-dev

am: f1a93c225b

Change-Id: I60d258ddb8fd0bf9632f1f70c5cdeb3078938059

1 files changed, 0 insertions, 306 deletions

diff --git a/user_activity_benchmarks/benchmark_metrics.py b/user_activity_benchmarks/benchmark_metrics.py
deleted file mode 100644
index 30ae31e0..00000000
--- a/user_activity_benchmarks/benchmark_metrics.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright 2016 The Chromium OS Authors. All rights reserved.
-# Use of this source code is governed by a BSD-style license that can be
-# found in the LICENSE file.
-"""Computes the metrics for functions, Chrome OS components and benchmarks."""
-
-from collections import defaultdict
-
-
-def ComputeDistanceForFunction(child_functions_statistics_sample,
-                               child_functions_statistics_reference):
-  """Computes the distance metric for a function.
-
-  Args:
-    child_functions_statistics_sample: A dict that has as a key the name of a
-      function and as a value the inclusive count fraction. The keys are
-      the child functions of a sample parent function.
-    child_functions_statistics_reference: A dict that has as a key the name of
-      a function and as a value the inclusive count fraction. The keys are
-      the child functions of a reference parent function.
-
-  Returns:
-    A float value representing the sum of inclusive count fraction
-    differences of pairs of common child functions. If a child function is
-    present in a single data set, then we consider the missing inclusive
-    count fraction as 0. This value describes the difference in behaviour
-    between a sample and the reference parent function.
-  """
-  # We initialize the distance with a small value to avoid the further
-  # division by zero.
-  distance = 1.0
-
-  for child_function, inclusive_count_fraction_reference in \
-      child_functions_statistics_reference.iteritems():
-    inclusive_count_fraction_sample = 0.0
-
-    if child_function in child_functions_statistics_sample:
-      inclusive_count_fraction_sample = \
-          child_functions_statistics_sample[child_function]
-    distance += \
-        abs(inclusive_count_fraction_sample -
-            inclusive_count_fraction_reference)
-
-  for child_function, inclusive_count_fraction_sample in \
-      child_functions_statistics_sample.iteritems():
-    if child_function not in child_functions_statistics_reference:
-      distance += inclusive_count_fraction_sample
-
-  return distance
-
-
-def ComputeScoreForFunction(distance, reference_fraction, sample_fraction):
-  """Computes the score for a function.
-
-  Args:
-    distance: A float value representing the difference in behaviour between
-      the sample and the reference function.
-    reference_fraction: A float value representing the inclusive count
-      fraction of the reference function.
-    sample_fraction: A float value representing the inclusive count
-      fraction of the sample function.
-
-  Returns:
-    A float value representing the score of the function.
-  """
-  return reference_fraction * sample_fraction / distance
-
-
-def ComputeMetricsForComponents(cwp_function_groups, function_metrics):
-  """Computes the metrics for a set of Chrome OS components.
-
-  For every Chrome OS group, we compute the number of functions matching the
-  group, the cumulative and average score, the cumulative and average distance
-  of all those functions. A function matches a group if the path of the file
-  containing its definition contains the common path describing the group.
-
-  Args:
-    cwp_function_groups: A dict having as a key the name of the group and as a
-      value a common path describing the group.
-    function_metrics: A dict having as a key the name of the function and the
-      name of the file where it is declared concatenated by a ',', and as a
-      value a tuple containing the distance and the score metrics.
-
-  Returns:
-    A dict containing as a key the name of the group and as a value a tuple
-    with the group file path, the number of functions matching the group,
-    the cumulative and average score, cumulative and average distance of all
-    those functions.
-  """
-  function_groups_metrics = defaultdict(lambda: (0, 0.0, 0.0, 0.0, 0.0))
-
-  for function_key, metric in function_metrics.iteritems():
-    _, function_file = function_key.split(',')
-
-    for group, common_path in cwp_function_groups:
-      if common_path not in function_file:
-        continue
-
-      function_distance = metric[0]
-      function_score = metric[1]
-      group_statistic = function_groups_metrics[group]
-
-      function_count = group_statistic[1] + 1
-      function_distance_cum = function_distance + group_statistic[2]
-      function_distance_avg = function_distance_cum / float(function_count)
-      function_score_cum = function_score + group_statistic[4]
-      function_score_avg = function_score_cum / float(function_count)
-
-      function_groups_metrics[group] = \
-          (common_path,
-           function_count,
-           function_distance_cum,
-           function_distance_avg,
-           function_score_cum,
-           function_score_avg)
-      break
-
-  return function_groups_metrics
-
-
-def ComputeMetricsForBenchmark(function_metrics):
-  function_count = len(function_metrics.keys())
-  distance_cum = 0.0
-  distance_avg = 0.0
-  score_cum = 0.0
-  score_avg = 0.0
-
-  for distance, score in function_metrics.values():
-    distance_cum += distance
-    score_cum += score
-
-  distance_avg = distance_cum / float(function_count)
-  score_avg = score_cum / float(function_count)
-  return function_count, distance_cum, distance_avg, score_cum, score_avg
-
-
-def ComputeFunctionCountForBenchmarkSet(set_function_metrics, cwp_functions,
-                                        metric_string):
-  """Computes the function count metric pair for the benchmark set.
-
-     For the function count metric, we count the unique functions covered by the
-     set of benchmarks. We compute the fraction of unique functions out
-     of the amount of CWP functions given.
-
-     We compute also the same metric pair for every group from the keys of the
-     set_function_metrics dict.
-
-  Args:
-    set_function_metrics: A list of dicts having as a key the name of a group
-      and as value a list of functions that match the given group.
-    cwp_functions: A dict having as a key the name of the groups and as a value
-      the list of CWP functions that match an individual group.
-    metric_string: A tuple of strings that will be mapped to the tuple of metric
-      values in the returned function group dict. This is done for convenience
-      for the JSON output.
-
-  Returns:
-    A tuple with the metric pair and a dict with the group names and values
-    of the metric pair. The first value of the metric pair represents the
-    function count and the second value the function count fraction.
-    The dict has as a key the name of the group and as a value a dict that
-    maps the metric_string  to the values of the metric pair of the group.
-  """
-  cwp_functions_count = sum(len(functions)
-                            for functions in cwp_functions.itervalues())
-  set_groups_functions = defaultdict(set)
-  for benchmark_function_metrics in set_function_metrics:
-    for group_name in benchmark_function_metrics:
-      set_groups_functions[group_name] |= \
-          set(benchmark_function_metrics[group_name])
-
-  set_groups_functions_count = {}
-  set_functions_count = 0
-  for group_name, functions \
-      in set_groups_functions.iteritems():
-    set_group_functions_count = len(functions)
-    if group_name in cwp_functions:
-      set_groups_functions_count[group_name] = {
-          metric_string[0]: set_group_functions_count,
-          metric_string[1]:
-          set_group_functions_count / float(len(cwp_functions[group_name]))}
-    else:
-      set_groups_functions_count[group_name] = \
-          {metric_string[0]: set_group_functions_count, metric_string[1]: 0.0}
-    set_functions_count += set_group_functions_count
-
-  set_functions_count_fraction = \
-      set_functions_count / float(cwp_functions_count)
-  return (set_functions_count, set_functions_count_fraction), \
-      set_groups_functions_count
-
-
-def ComputeDistanceForBenchmarkSet(set_function_metrics, cwp_functions,
-                                   metric_string):
-  """Computes the distance variation metric pair for the benchmark set.
-
-     For the distance variation metric, we compute the sum of the distance
-     variations of the functions covered by a set of benchmarks.
-     We define the distance variation as the difference between the distance
-     value of a functions and the ideal distance value (1.0).
-     If a function appears in multiple common functions files, we consider
-     only the minimum value. We compute also the distance variation per
-     function.
-
-     In addition, we compute also the same metric pair for every group from
-     the keys of the set_function_metrics dict.
-
-  Args:
-    set_function_metrics: A list of dicts having as a key the name of a group
-      and as value a list of functions that match the given group.
-    cwp_functions: A dict having as a key the name of the groups and as a value
-      the list of CWP functions that match an individual group.
-    metric_string: A tuple of strings that will be mapped to the tuple of metric
-      values in the returned function group dict. This is done for convenience
-      for the JSON output.
-
-  Returns:
-    A tuple with the metric pair and a dict with the group names and values
-    of the metric pair. The first value of the metric pair represents the
-    distance variation per function and the second value the distance variation.
-    The dict has as a key the name of the group and as a value a dict that
-    maps the metric_string to the values of the metric pair of the group.
-  """
-  set_unique_functions = defaultdict(lambda: defaultdict(lambda: float('inf')))
-  set_function_count = 0
-  total_distance_variation = 0.0
-  for benchmark_function_metrics in set_function_metrics:
-    for group_name in benchmark_function_metrics:
-      for function_key, metrics in \
-          benchmark_function_metrics[group_name].iteritems():
-        previous_distance = \
-            set_unique_functions[group_name][function_key]
-        min_distance = min(metrics[0], previous_distance)
-        set_unique_functions[group_name][function_key] = min_distance
-  groups_distance_variations = defaultdict(lambda: (0.0, 0.0))
-  for group_name, functions_distances in set_unique_functions.iteritems():
-    group_function_count = len(functions_distances)
-    group_distance_variation = \
-        sum(functions_distances.itervalues()) - float(group_function_count)
-    total_distance_variation += group_distance_variation
-    set_function_count += group_function_count
-    groups_distance_variations[group_name] = \
-        {metric_string[0]:
-         group_distance_variation / float(group_function_count),
-         metric_string[1]: group_distance_variation}
-
-  return (total_distance_variation / set_function_count,
-          total_distance_variation), groups_distance_variations
-
-
-def ComputeScoreForBenchmarkSet(set_function_metrics, cwp_functions,
-                                metric_string):
-  """Computes the function count metric pair for the benchmark set.
-
-     For the score metric, we compute the sum of the scores of the functions
-     from a set of benchmarks. If a function appears in multiple common
-     functions files, we consider only the maximum value. We compute also the
-     fraction of this sum from the sum of all the scores of the functions from
-     the CWP data covering the given groups, in the ideal case (the ideal
-     score of a function is 1.0).
-
-     In addition, we compute the same metric pair for every group from the
-     keys of the set_function_metrics dict.
-
-  Args:
-    set_function_metrics: A list of dicts having as a key the name of a group
-      and as value a list of functions that match the given group.
-    cwp_functions: A dict having as a key the name of the groups and as a value
-      the list of CWP functions that match an individual group.
-    metric_string: A tuple of strings that will be mapped to the tuple of metric
-      values in the returned function group dict. This is done for convenience
-      for the JSON output.
-
-  Returns:
-    A tuple with the metric pair and a dict with the group names and values
-    of the metric pair. The first value of the pair is the fraction of the sum
-    of the scores from the ideal case and the second value represents the
-    sum of scores of the functions. The dict has as a key the name of the group
-    and as a value a dict that maps the metric_string to the values of the
-    metric pair of the group.
-  """
-  cwp_functions_count = sum(len(functions)
-                            for functions in cwp_functions.itervalues())
-  set_unique_functions = defaultdict(lambda: defaultdict(lambda: 0.0))
-  total_score = 0.0
-
-  for benchmark_function_metrics in set_function_metrics:
-    for group_name in benchmark_function_metrics:
-      for function_key, metrics in \
-          benchmark_function_metrics[group_name].iteritems():
-        previous_score = \
-            set_unique_functions[group_name][function_key]
-        max_score = max(metrics[1], previous_score)
-        set_unique_functions[group_name][function_key] = max_score
-
-  groups_scores = defaultdict(lambda: (0.0, 0.0))
-
-  for group_name, function_scores in set_unique_functions.iteritems():
-    group_function_count = float(len(cwp_functions[group_name]))
-    group_score = sum(function_scores.itervalues())
-    total_score += group_score
-    groups_scores[group_name] = {
-        metric_string[0]: group_score / group_function_count,
-        metric_string[1]: group_score
-    }
-
-  return (total_score / cwp_functions_count, total_score), groups_scores