Use a different algorithm with the low entropy source for field trials.

chrome/common/metrics/entropy_provider_unittest.cc

+// Copyright (c) 2012 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.
+
+#include <cmath>
+#include <limits>
+#include <numeric>
+
+#include "base/basictypes.h"
+#include "base/guid.h"
+#include "base/memory/scoped_ptr.h"
+#include "base/rand_util.h"
+#include "base/string_number_conversions.h"
+#include "chrome/common/metrics/entropy_provider.h"
+#include "testing/gtest/include/gtest/gtest.h"
+
+namespace metrics {
+
+namespace {
+
+// Size of the low entropy source to use for the permuted entropy provider
+// in tests.
+const size_t kMaxLowEntropySize = (1 << 13);
+
+// Field trial names used in unit tests.
+const std::string kTestTrialNames[] = { "TestTrial", "AnotherTestTrial",
+                                        "NewTabButton" };
+
+// Computes the Chi-Square statistic for |values| assuming they follow a uniform
+// distribution, where each entry has expected value |expected_value|.
+//
+// The Chi-Square statistic is defined as Sum((O-E)^2/E) where O is the observed
+// value and E is the expected value.
+double ComputeChiSquare(const std::vector<int>& values,
+                        double expected_value) {
+  double sum = 0;
+  for (size_t i = 0; i < values.size(); ++i) {
+    const double delta = values[i] - expected_value;
+    sum += (delta * delta) / expected_value;
+  }
+  return sum;
+}
+
+// Computes SHA1-based entropy for the given |trial_name| based on
+// |entropy_source|
+double GenerateSHA1Entropy(const std::string& entropy_source,
+                           const std::string& trial_name) {
+  SHA1EntropyProvider sha1_provider(entropy_source);
+  return sha1_provider.GetEntropyForTrial(trial_name);
+}
+
+// Generates permutation-based entropy for the given |trial_name| based on
+// |entropy_source| which must be in the range [0, entropy_max).
+double GeneratePermutedEntropy(uint16 entropy_source,
+                               size_t entropy_max,
+                               const std::string& trial_name) {
+  PermutedEntropyProvider permuted_provider(entropy_source, entropy_max);
+  return permuted_provider.GetEntropyForTrial(trial_name);
+}
+
+// Helper interface for testing used to generate entropy values for a given
+// field trial. Unlike EntropyProvider, which keeps the low/high entropy source
+// value constant and generates entropy for different trial names, instances
+// of TrialEntropyGenerator keep the trial name constant and generate low/high
+// entropy source values internally to produce each output entropy value.
+class TrialEntropyGenerator {
+ public:
+  virtual ~TrialEntropyGenerator() {}
+  virtual double GenerateEntropyValue() const = 0;
+};
+
+// An TrialEntropyGenerator that uses the SHA1EntropyProvider with the high
+// entropy source (random GUID with 128 bits of entropy + 13 additional bits of
+// entropy corresponding to a low entropy source).
+class SHA1EntropyGenerator : public TrialEntropyGenerator {
+ public:
+  explicit SHA1EntropyGenerator(const std::string& trial_name)
+      : trial_name_(trial_name) {
+  }
+
+  ~SHA1EntropyGenerator() {
+  }
+
+  virtual double GenerateEntropyValue() const OVERRIDE {
+    // Use a random GUID + 13 additional bits of entropy to match how the
+    // SHA1EntropyProvider is used in metrics_service.cc.
+    const int low_entropy_source =
+        static_cast<uint16>(base::RandInt(0, kMaxLowEntropySize - 1));
+    const std::string high_entropy_source =
+        base::GenerateGUID() + base::IntToString(low_entropy_source);
+    return GenerateSHA1Entropy(high_entropy_source, trial_name_);
+  }
+
+ private:
+  const std::string& trial_name_;
+
+  DISALLOW_COPY_AND_ASSIGN(SHA1EntropyGenerator);
+};
+
+// An TrialEntropyGenerator that uses the permuted entropy provider algorithm,
+// using 13-bit low entropy source values.
+class PermutedEntropyGenerator : public TrialEntropyGenerator {
+ public:
+  explicit PermutedEntropyGenerator(const std::string& trial_name)
+      : mapping_(kMaxLowEntropySize) {
+    // Note: Given a trial name, the computed mapping will be the same.
+    // As a performance optimization, pre-compute the mapping once per trial
+    // name and index into it for each entropy value.
+    internal::PermuteMappingUsingTrialName(trial_name, &mapping_);
+  }
+
+  ~PermutedEntropyGenerator() {
+  }
+
+  virtual double GenerateEntropyValue() const OVERRIDE {
+    const int low_entropy_source =
+        static_cast<uint16>(base::RandInt(0, kMaxLowEntropySize - 1));
+    return mapping_[low_entropy_source] /
+           static_cast<double>(kMaxLowEntropySize);
+  }
+
+ private:
+  std::vector<uint16> mapping_;
+
+  DISALLOW_COPY_AND_ASSIGN(PermutedEntropyGenerator);
+};
+
+// Tests uniformity of a given |entropy_generator| using the Chi-Square Goodness
+// of Fit Test.
+void PerformEntropyUniformityTest(
+    const std::string& trial_name,
+    const TrialEntropyGenerator& entropy_generator) {
+  // Number of buckets in the simulated field trials.
+  const size_t kBucketCount = 20;
+  // Max number of iterations to perform before giving up and failing.
+  const size_t kMaxIterationCount = 100000;
+  // The number of iterations to perform before each time the statistical
+  // significance of the results is checked.
+  const size_t kCheckIterationCount = 10000;
+  // This is the Chi-Square threshold from the Chi-Square statistic table for
+  // 19 degrees of freedom (based on |kBucketCount|) with a 99.9% confidence
+  // level. See: http://www.medcalc.org/manual/chi-square-table.php
+  const double kChiSquareThreshold = 43.82;
+
+  std::vector<int> distribution(kBucketCount);
+
+  for (size_t i = 1; i <= kMaxIterationCount; ++i) {
+    const double entropy_value = entropy_generator.GenerateEntropyValue();
+    const size_t bucket = static_cast<size_t>(kBucketCount * entropy_value);
+    ASSERT_LT(bucket, kBucketCount);
+    distribution[bucket] += 1;
+
+    // After |kCheckIterationCount| iterations, compute the Chi-Square
+    // statistic of the distribution. If the resulting statistic is greater
+    // than |kChiSquareThreshold|, we can conclude with 99.9% confidence
+    // that the observed samples do not follow a uniform distribution.
+    //
+    // However, since 99.9% would still result in a false negative every
+    // 1000 runs of the test, do not treat it as a failure (else the test
+    // will be flaky). Instead, perform additional iterations to determine
+    // if the distribution will converge, up to |kMaxIterationCount|.
+    if ((i % kCheckIterationCount) == 0) {
+      const double expected_value_per_bucket =
+          static_cast<double>(i) / kBucketCount;
+      const double chi_square =
+          ComputeChiSquare(distribution, expected_value_per_bucket);
+      if (chi_square < kChiSquareThreshold)
+        break;
+
+      // If |i == kMaxIterationCount|, the Chi-Square statistic did not
+      // converge after |kMaxIterationCount|.
+      EXPECT_NE(i, kMaxIterationCount) << "Failed for trial " <<
+          trial_name << " with chi_square = " << chi_square <<
+          " after " << kMaxIterationCount << " iterations.";
+    }
+  }
+}
+
+}  // namespace
+
+class EntropyProviderTest : public testing::Test {
+};
+
+TEST_F(EntropyProviderTest, UseOneTimeRandomizationSHA1) {
+  // Simply asserts that two trials using one-time randomization
+  // that have different names, normally generate different results.
+  //
+  // Note that depending on the one-time random initialization, they
+  // _might_ actually give the same result, but we know that given
+  // the particular client_id we use for unit tests they won't.
+  base::FieldTrialList field_trial_list(new SHA1EntropyProvider("client_id"));
+  scoped_refptr<base::FieldTrial> trials[] = {
+      base::FieldTrialList::FactoryGetFieldTrial("one", 100, "default",
+          base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL),
+      base::FieldTrialList::FactoryGetFieldTrial("two", 100, "default",
+          base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL) };
+
+  for (size_t i = 0; i < arraysize(trials); ++i) {
+    trials[i]->UseOneTimeRandomization();
+
+    for (int j = 0; j < 100; ++j)
+      trials[i]->AppendGroup("", 1);
+  }
+
+  // The trials are most likely to give different results since they have
+  // different names.
+  EXPECT_NE(trials[0]->group(), trials[1]->group());
+  EXPECT_NE(trials[0]->group_name(), trials[1]->group_name());
+}
+
+TEST_F(EntropyProviderTest, UseOneTimeRandomizationPermuted) {
+  // Simply asserts that two trials using one-time randomization
+  // that have different names, normally generate different results.
+  //
+  // Note that depending on the one-time random initialization, they
+  // _might_ actually give the same result, but we know that given
+  // the particular client_id we use for unit tests they won't.
+  base::FieldTrialList field_trial_list(
+      new PermutedEntropyProvider(1234, kMaxLowEntropySize));
+  scoped_refptr<base::FieldTrial> trials[] = {
+      base::FieldTrialList::FactoryGetFieldTrial("one", 100, "default",
+          base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL),
+      base::FieldTrialList::FactoryGetFieldTrial("two", 100, "default",
+          base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL) };
+
+  for (size_t i = 0; i < arraysize(trials); ++i) {
+    trials[i]->UseOneTimeRandomization();
+
+    for (int j = 0; j < 100; ++j)
+      trials[i]->AppendGroup("", 1);
+  }
+
+  // The trials are most likely to give different results since they have
+  // different names.
+  EXPECT_NE(trials[0]->group(), trials[1]->group());
+  EXPECT_NE(trials[0]->group_name(), trials[1]->group_name());
+}
+
+TEST_F(EntropyProviderTest, SHA1Entropy) {
+  const double results[] = { GenerateSHA1Entropy("hi", "1"),
+                             GenerateSHA1Entropy("there", "1") };
+
+  EXPECT_NE(results[0], results[1]);
+  for (size_t i = 0; i < arraysize(results); ++i) {
+    EXPECT_LE(0.0, results[i]);
+    EXPECT_GT(1.0, results[i]);
+  }
+
+  EXPECT_EQ(GenerateSHA1Entropy("yo", "1"),
+            GenerateSHA1Entropy("yo", "1"));
+  EXPECT_NE(GenerateSHA1Entropy("yo", "something"),
+            GenerateSHA1Entropy("yo", "else"));
+}
+
+TEST_F(EntropyProviderTest, PermutedEntropy) {
+  const double results[] = {
+      GeneratePermutedEntropy(1234, kMaxLowEntropySize, "1"),
+      GeneratePermutedEntropy(4321, kMaxLowEntropySize, "1") };
+
+  EXPECT_NE(results[0], results[1]);
+  for (size_t i = 0; i < arraysize(results); ++i) {
+    EXPECT_LE(0.0, results[i]);
+    EXPECT_GT(1.0, results[i]);
+  }
+
+  EXPECT_EQ(GeneratePermutedEntropy(1234, kMaxLowEntropySize, "1"),
+            GeneratePermutedEntropy(1234, kMaxLowEntropySize, "1"));
+  EXPECT_NE(GeneratePermutedEntropy(1234, kMaxLowEntropySize, "something"),
+            GeneratePermutedEntropy(1234, kMaxLowEntropySize, "else"));
+}
+
+TEST_F(EntropyProviderTest, PermutedEntropyProviderResults) {
+  // Verifies that PermutedEntropyProvider produces expected results. This
+  // ensures that the results are the same between platforms and ensures that
+  // changes to the implementation do not regress this accidentally.
+
+  EXPECT_DOUBLE_EQ(2194 / static_cast<double>(kMaxLowEntropySize),
+                   GeneratePermutedEntropy(1234, kMaxLowEntropySize, "XYZ"));
+  EXPECT_DOUBLE_EQ(5676 / static_cast<double>(kMaxLowEntropySize),
+                   GeneratePermutedEntropy(1, kMaxLowEntropySize, "Test"));
+  EXPECT_DOUBLE_EQ(1151 / static_cast<double>(kMaxLowEntropySize),
+                   GeneratePermutedEntropy(5000, kMaxLowEntropySize, "Foo"));
+}
+
+TEST_F(EntropyProviderTest, SHA1EntropyIsUniform) {
+  for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
+    SHA1EntropyGenerator entropy_generator(kTestTrialNames[i]);
+    PerformEntropyUniformityTest(kTestTrialNames[i], entropy_generator);
+  }
+}
+
+TEST_F(EntropyProviderTest, PermutedEntropyIsUniform) {
+  for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
+    PermutedEntropyGenerator entropy_generator(kTestTrialNames[i]);
+    PerformEntropyUniformityTest(kTestTrialNames[i], entropy_generator);
+  }
+}
+
+TEST_F(EntropyProviderTest, SeededRandGeneratorIsUniform) {
+  // Verifies that SeededRandGenerator has a uniform distribution.
+  //
+  // Mirrors RandUtilTest.RandGeneratorIsUniform in base/rand_util_unittest.cc.
+
+  const uint32 kTopOfRange = (std::numeric_limits<uint32>::max() / 4ULL) * 3ULL;
+  const uint32 kExpectedAverage = kTopOfRange / 2ULL;
+  const uint32 kAllowedVariance = kExpectedAverage / 50ULL;  // +/- 2%
+  const int kMinAttempts = 1000;
+  const int kMaxAttempts = 1000000;
+
+  for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
+    const uint32 seed = internal::HashName(kTestTrialNames[i]);
+    internal::SeededRandGenerator rand_generator(seed);
+
+    double cumulative_average = 0.0;
+    int count = 0;
+    while (count < kMaxAttempts) {
+      uint32 value = rand_generator(kTopOfRange);
+      cumulative_average = (count * cumulative_average + value) / (count + 1);
+
+      // Don't quit too quickly for things to start converging, or we may have
+      // a false positive.
+      if (count > kMinAttempts &&
+          kExpectedAverage - kAllowedVariance < cumulative_average &&
+          cumulative_average < kExpectedAverage + kAllowedVariance) {
+        break;
+      }
+
+      ++count;
+    }
+
+    ASSERT_LT(count, kMaxAttempts) << "Expected average was " <<
+        kExpectedAverage << ", average ended at " << cumulative_average <<
+        ", for trial " << kTestTrialNames[i];
+  }
+}
+
+}  // namespace metrics