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// 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 "components/variations/entropy_provider.h"
#include <stddef.h>
#include <stdint.h>
#include <cmath>
#include <limits>
#include <memory>
#include <numeric>
#include "base/guid.h"
#include "base/macros.h"
#include "base/rand_util.h"
#include "base/strings/string_number_conversions.h"
#include "components/variations/hashing.h"
#include "testing/gtest/include/gtest/gtest.h"
namespace variations {
namespace {
// Size of the low entropy source to use for the permuted entropy provider
// in tests.
const size_t kMaxLowEntropySize = 8000;
// Field trial names used in unit tests.
const char* const 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, 0);
}
// 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_t entropy_source,
size_t entropy_max,
const std::string& trial_name) {
PermutedEntropyProvider permuted_provider(entropy_source, entropy_max);
return permuted_provider.GetEntropyForTrial(trial_name, 0);
}
// 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() override {}
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_t>(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:
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.
const uint32_t randomization_seed = HashName(trial_name);
internal::PermuteMappingUsingRandomizationSeed(randomization_seed,
&mapping_);
}
~PermutedEntropyGenerator() override {}
double GenerateEntropyValue() const override {
const int low_entropy_source =
static_cast<uint16_t>(base::RandInt(0, kMaxLowEntropySize - 1));
return mapping_[low_entropy_source] /
static_cast<double>(kMaxLowEntropySize);
}
private:
std::vector<uint16_t> 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
TEST(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(
std::make_unique<SHA1EntropyProvider>("client_id"));
const int kNoExpirationYear = base::FieldTrialList::kNoExpirationYear;
scoped_refptr<base::FieldTrial> trials[] = {
base::FieldTrialList::FactoryGetFieldTrial(
"one", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, nullptr),
base::FieldTrialList::FactoryGetFieldTrial(
"two", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, nullptr),
};
for (size_t i = 0; i < arraysize(trials); ++i) {
for (int j = 0; j < 100; ++j)
trials[i]->AppendGroup(std::string(), 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(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(
std::make_unique<PermutedEntropyProvider>(1234, kMaxLowEntropySize));
const int kNoExpirationYear = base::FieldTrialList::kNoExpirationYear;
scoped_refptr<base::FieldTrial> trials[] = {
base::FieldTrialList::FactoryGetFieldTrial(
"one", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, nullptr),
base::FieldTrialList::FactoryGetFieldTrial(
"two", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, nullptr),
};
for (size_t i = 0; i < arraysize(trials); ++i) {
for (int j = 0; j < 100; ++j)
trials[i]->AppendGroup(std::string(), 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(EntropyProviderTest, UseOneTimeRandomizationWithCustomSeedPermuted) {
// Ensures that two trials with different names but the same custom seed used
// for one time randomization produce the same group assignments.
base::FieldTrialList field_trial_list(
std::make_unique<PermutedEntropyProvider>(1234, kMaxLowEntropySize));
const int kNoExpirationYear = base::FieldTrialList::kNoExpirationYear;
const uint32_t kCustomSeed = 9001;
scoped_refptr<base::FieldTrial> trials[] = {
base::FieldTrialList::FactoryGetFieldTrialWithRandomizationSeed(
"one", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, kCustomSeed, nullptr, nullptr),
base::FieldTrialList::FactoryGetFieldTrialWithRandomizationSeed(
"two", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, kCustomSeed, nullptr, nullptr),
};
for (size_t i = 0; i < arraysize(trials); ++i) {
for (int j = 0; j < 100; ++j)
trials[i]->AppendGroup(std::string(), 1);
}
// Normally, these trials should produce different groups, but if the same
// custom seed is used, they should produce the same group assignment.
EXPECT_EQ(trials[0]->group(), trials[1]->group());
EXPECT_EQ(trials[0]->group_name(), trials[1]->group_name());
}
TEST(EntropyProviderTest, UseOneTimeRandomizationWithCustomSeedSHA1) {
// Ensures that two trials with different names but the same custom seed used
// for one time randomization produce the same group assignments.
base::FieldTrialList field_trial_list(
std::make_unique<SHA1EntropyProvider>("client_id"));
const int kNoExpirationYear = base::FieldTrialList::kNoExpirationYear;
const uint32_t kCustomSeed = 9001;
scoped_refptr<base::FieldTrial> trials[] = {
base::FieldTrialList::FactoryGetFieldTrialWithRandomizationSeed(
"one", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, kCustomSeed, nullptr, nullptr),
base::FieldTrialList::FactoryGetFieldTrialWithRandomizationSeed(
"two", 100, "default", kNoExpirationYear, 1, 1,
base::FieldTrial::ONE_TIME_RANDOMIZED, kCustomSeed, nullptr, nullptr),
};
for (size_t i = 0; i < arraysize(trials); ++i) {
for (int j = 0; j < 100; ++j)
trials[i]->AppendGroup(std::string(), 1);
}
// Normally, these trials should produce different groups, but if the same
// custom seed is used, they should produce the same group assignment.
EXPECT_EQ(trials[0]->group(), trials[1]->group());
EXPECT_EQ(trials[0]->group_name(), trials[1]->group_name());
}
TEST(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(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(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(EntropyProviderTest, SHA1EntropyIsUniform) {
for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
SHA1EntropyGenerator entropy_generator(kTestTrialNames[i]);
PerformEntropyUniformityTest(kTestTrialNames[i], entropy_generator);
}
}
TEST(EntropyProviderTest, PermutedEntropyIsUniform) {
for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
PermutedEntropyGenerator entropy_generator(kTestTrialNames[i]);
PerformEntropyUniformityTest(kTestTrialNames[i], entropy_generator);
}
}
TEST(EntropyProviderTest, SeededRandGeneratorIsUniform) {
// Verifies that SeededRandGenerator has a uniform distribution.
//
// Mirrors RandUtilTest.RandGeneratorIsUniform in base/rand_util_unittest.cc.
const uint32_t kTopOfRange =
(std::numeric_limits<uint32_t>::max() / 4ULL) * 3ULL;
const uint32_t kExpectedAverage = kTopOfRange / 2ULL;
const uint32_t kAllowedVariance = kExpectedAverage / 50ULL; // +/- 2%
const int kMinAttempts = 1000;
const int kMaxAttempts = 1000000;
for (size_t i = 0; i < arraysize(kTestTrialNames); ++i) {
const uint32_t seed = HashName(kTestTrialNames[i]);
internal::SeededRandGenerator rand_generator(seed);
double cumulative_average = 0.0;
int count = 0;
while (count < kMaxAttempts) {
uint32_t 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 variations