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| 1 // Copyright (c) 2012 The Chromium Authors. All rights reserved. | |
| 2 // Use of this source code is governed by a BSD-style license that can be | |
| 3 // found in the LICENSE file. | |
| 4 | |
| 5 #include <limits> | |
| 6 | |
| 7 #include "base/basictypes.h" | |
| 8 #include "base/memory/scoped_ptr.h" | |
| 9 #include "base/rand_util.h" | |
| 10 #include "base/string_number_conversions.h" | |
| 11 #include "chrome/common/metrics/entropy_provider.h" | |
| 12 #include "testing/gtest/include/gtest/gtest.h" | |
| 13 | |
| 14 class EntropyProviderTest : public testing::Test { | |
| 15 public: | |
| 16 // Computes SHA1-based entropy for the given |trial_name| based on | |
| 17 // |entropy_source| | |
| 18 double GenerateSHA1Entropy(const std::string& entropy_source, | |
| 19 const std::string& trial_name) { | |
| 20 SHA1EntropyProvider sha1_provider(entropy_source); | |
| 21 return sha1_provider.GetEntropyForTrial(trial_name); | |
| 22 } | |
| 23 | |
| 24 // Generates permutation-based entropy for the given |trial_name| based on | |
| 25 // |entropy_source| which must be in the range [0, entropy_max). | |
| 26 double GeneratePermutedEntropy(uint16 entropy_source, | |
| 27 size_t entropy_max, | |
| 28 const std::string& trial_name) { | |
| 29 PermutedEntropyProvider permuted_provider(entropy_source, entropy_max); | |
| 30 return permuted_provider.GetEntropyForTrial(trial_name); | |
| 31 } | |
| 32 }; | |
| 33 | |
| 34 | |
| 35 TEST_F(EntropyProviderTest, UseOneTimeRandomizationSHA1) { | |
| 36 // Simply asserts that two trials using one-time randomization | |
| 37 // that have different names, normally generate different results. | |
| 38 // | |
| 39 // Note that depending on the one-time random initialization, they | |
| 40 // _might_ actually give the same result, but we know that given | |
| 41 // the particular client_id we use for unit tests they won't. | |
| 42 base::FieldTrialList field_trial_list(new SHA1EntropyProvider("client_id")); | |
| 43 scoped_refptr<base::FieldTrial> trials[] = { | |
| 44 base::FieldTrialList::FactoryGetFieldTrial("one", 100, "default", | |
|
jar (doing other things)
2012/08/17 19:06:19
nit: 4 character indent (I think) for this line ov
Alexei Svitkine (slow)
2012/08/20 15:57:40
Done.
| |
| 45 base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL), | |
| 46 base::FieldTrialList::FactoryGetFieldTrial("two", 100, "default", | |
| 47 base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL), | |
| 48 }; | |
|
jar (doing other things)
2012/08/17 19:06:19
nit: This curly is not a block closure, and is mor
Alexei Svitkine (slow)
2012/08/20 15:57:40
That suggestion doesn't seem consistent with the s
jar (doing other things)
2012/08/23 01:38:30
I checked with BrettW, and WillChan (Google readab
Alexei Svitkine (slow)
2012/08/23 15:10:43
Done.
| |
| 49 | |
| 50 for (size_t i = 0; i < arraysize(trials); ++i) { | |
| 51 trials[i]->UseOneTimeRandomization(); | |
| 52 | |
| 53 for (int j = 0; j < 100; ++j) | |
| 54 trials[i]->AppendGroup("", 1); | |
| 55 } | |
| 56 | |
| 57 // The trials are most likely to give different results since they have | |
| 58 // different names. | |
| 59 ASSERT_NE(trials[0]->group(), trials[1]->group()); | |
| 60 ASSERT_NE(trials[0]->group_name(), trials[1]->group_name()); | |
| 61 } | |
| 62 | |
| 63 TEST_F(EntropyProviderTest, UseOneTimeRandomizationPermuted) { | |
| 64 // Simply asserts that two trials using one-time randomization | |
| 65 // that have different names, normally generate different results. | |
| 66 // | |
| 67 // Note that depending on the one-time random initialization, they | |
| 68 // _might_ actually give the same result, but we know that given | |
| 69 // the particular client_id we use for unit tests they won't. | |
| 70 const size_t kMaxEntropySize = (1 << 13); | |
| 71 base::FieldTrialList field_trial_list( | |
| 72 new PermutedEntropyProvider(1234, kMaxEntropySize)); | |
| 73 scoped_refptr<base::FieldTrial> trials[] = { | |
| 74 base::FieldTrialList::FactoryGetFieldTrial("one", 100, "default", | |
|
jar (doing other things)
2012/08/17 19:06:19
nit: 4 character indent
Alexei Svitkine (slow)
2012/08/20 15:57:40
Done.
| |
| 75 base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL), | |
| 76 base::FieldTrialList::FactoryGetFieldTrial("two", 100, "default", | |
| 77 base::FieldTrialList::kExpirationYearInFuture, 1, 1, NULL), | |
| 78 }; | |
|
jar (doing other things)
2012/08/17 19:06:19
nit: This curly is not a block closure, and is mor
Alexei Svitkine (slow)
2012/08/20 15:57:40
That suggestion doesn't seem consistent with the s
| |
| 79 | |
| 80 for (size_t i = 0; i < arraysize(trials); ++i) { | |
| 81 trials[i]->UseOneTimeRandomization(); | |
| 82 | |
| 83 for (int j = 0; j < 100; ++j) | |
| 84 trials[i]->AppendGroup("", 1); | |
| 85 } | |
| 86 | |
| 87 // The trials are most likely to give different results since they have | |
| 88 // different names. | |
| 89 ASSERT_NE(trials[0]->group(), trials[1]->group()); | |
| 90 ASSERT_NE(trials[0]->group_name(), trials[1]->group_name()); | |
| 91 } | |
| 92 | |
| 93 TEST_F(EntropyProviderTest, SHA1Entropy) { | |
| 94 double results[] = { | |
| 95 GenerateSHA1Entropy("hi", "1"), | |
|
jar (doing other things)
2012/08/17 19:06:19
nit: indent 4.
Alexei Svitkine (slow)
2012/08/20 15:57:40
Done.
| |
| 96 GenerateSHA1Entropy("there", "1"), | |
| 97 }; | |
| 98 ASSERT_NE(results[0], results[1]); | |
| 99 for (size_t i = 0; i < arraysize(results); ++i) { | |
| 100 ASSERT_LE(0.0, results[i]); | |
| 101 ASSERT_GT(1.0, results[i]); | |
| 102 } | |
| 103 | |
| 104 ASSERT_EQ(GenerateSHA1Entropy("yo", "1"), | |
| 105 GenerateSHA1Entropy("yo", "1")); | |
| 106 ASSERT_NE(GenerateSHA1Entropy("yo", "something"), | |
| 107 GenerateSHA1Entropy("yo", "else")); | |
| 108 } | |
| 109 | |
| 110 TEST_F(EntropyProviderTest, PermutedEntropy) { | |
| 111 const size_t kMaxEntropySize = (1 << 13); | |
| 112 double results[] = { | |
| 113 GeneratePermutedEntropy(1234, kMaxEntropySize, "1"), | |
| 114 GeneratePermutedEntropy(4321, kMaxEntropySize, "1"), | |
| 115 }; | |
| 116 ASSERT_NE(results[0], results[1]); | |
| 117 for (size_t i = 0; i < arraysize(results); ++i) { | |
| 118 ASSERT_LE(0.0, results[i]); | |
| 119 ASSERT_GT(1.0, results[i]); | |
| 120 } | |
| 121 | |
| 122 ASSERT_EQ(GeneratePermutedEntropy(1234, kMaxEntropySize, "1"), | |
| 123 GeneratePermutedEntropy(1234, kMaxEntropySize, "1")); | |
| 124 ASSERT_NE(GeneratePermutedEntropy(1234, kMaxEntropySize, "something"), | |
| 125 GeneratePermutedEntropy(1234, kMaxEntropySize, "else")); | |
| 126 } | |
| 127 | |
| 128 TEST_F(EntropyProviderTest, SHA1EntropyIsUniform) { | |
| 129 // Choose a random start number but go sequentially from there, so | |
| 130 // that each test tries a different range but we never provide uniformly | |
| 131 // distributed input data. | |
| 132 int current_number = base::RandInt(0, std::numeric_limits<int>::max()); | |
| 133 | |
| 134 // The expected value of a random distribution is the average over all | |
| 135 // samples as the number of samples approaches infinity. For a uniform | |
| 136 // distribution from [0.0, 1.0) this would be 0.5. | |
| 137 // | |
| 138 // We do kSamplesBetweenChecks at a time and check if the value has converged | |
| 139 // to a narrow interval around 0.5. A non-uniform distribution would likely | |
| 140 // converge at something different, or not converge consistently within this | |
| 141 // range (i.e. the test would start timing out occasionally). | |
| 142 int kSamplesBetweenChecks = 300; | |
| 143 int num_samples = 0; | |
| 144 double total_value = 0.0; | |
| 145 while (true) { | |
| 146 for (int i = 0; i < kSamplesBetweenChecks; ++i) { | |
| 147 total_value += GenerateSHA1Entropy( | |
| 148 base::IntToString(current_number++), "salt"); | |
| 149 num_samples++; | |
| 150 } | |
| 151 | |
| 152 double average = total_value / num_samples; | |
| 153 double kExpectedMin = 0.48; | |
| 154 double kExpectedMax = 0.52; | |
| 155 | |
| 156 if (num_samples > 1000 && | |
| 157 (average < kExpectedMin || average > kExpectedMax)) { | |
| 158 // Only printed once we have enough samples that it's very unlikely | |
| 159 // things haven't converged. | |
| 160 printf("After %d samples, the average was %f, outside the expected\n" | |
| 161 "range (%f, %f). We will add more samples and check after every\n" | |
| 162 "%d samples. If the average does not converge, something\n" | |
| 163 "is broken. If it does converge, the test will pass.\n", | |
| 164 num_samples, average, | |
| 165 kExpectedMin, kExpectedMax, kSamplesBetweenChecks); | |
| 166 } else { | |
| 167 // Success. | |
| 168 break; | |
| 169 } | |
| 170 } | |
| 171 } | |
| 172 | |
| 173 TEST_F(EntropyProviderTest, PermutedEntropyIsUniform) { | |
| 174 // Choose a random start number but go sequentially from there, so | |
| 175 // that each test tries a different range but we never provide uniformly | |
| 176 // distributed input data. | |
| 177 const size_t kMaxEntropySize = (1 << 13); | |
| 178 int current_number = base::RandInt(0, kMaxEntropySize - 1); | |
| 179 | |
| 180 // The expected value of a random distribution is the average over all | |
| 181 // samples as the number of samples approaches infinity. For a uniform | |
| 182 // distribution from [0.0, 1.0) this would be 0.5. | |
|
hfung
2012/08/17 17:35:40
So you aren't testing the distribution among diffe
Alexei Svitkine (slow)
2012/08/17 17:57:40
Ilya had the same concern / suggestion. (Note: Thi
| |
| 183 // | |
| 184 // We do kSamplesBetweenChecks at a time and check if the value has converged | |
| 185 // to a narrow interval around 0.5. A non-uniform distribution would likely | |
| 186 // converge at something different, or not converge consistently within this | |
| 187 // range (i.e. the test would start timing out occasionally). | |
| 188 int kSamplesBetweenChecks = 300; | |
| 189 int num_samples = 0; | |
| 190 double total_value = 0.0; | |
| 191 while (true) { | |
| 192 for (int i = 0; i < kSamplesBetweenChecks; ++i) { | |
| 193 total_value += GeneratePermutedEntropy(current_number++ % kMaxEntropySize, | |
| 194 kMaxEntropySize, "salt"); | |
| 195 num_samples++; | |
| 196 } | |
| 197 | |
| 198 double average = total_value / num_samples; | |
| 199 double kExpectedMin = 0.48; | |
| 200 double kExpectedMax = 0.52; | |
| 201 | |
| 202 if (num_samples > 1000 && | |
| 203 (average < kExpectedMin || average > kExpectedMax)) { | |
| 204 // Only printed once we have enough samples that it's very unlikely | |
| 205 // things haven't converged. | |
| 206 printf("After %d samples, the average was %f, outside the expected\n" | |
| 207 "range (%f, %f). We will add more samples and check after every\n" | |
| 208 "%d samples. If the average does not converge, something\n" | |
| 209 "is broken. If it does converge, the test will pass.\n", | |
| 210 num_samples, average, | |
| 211 kExpectedMin, kExpectedMax, kSamplesBetweenChecks); | |
| 212 } else { | |
| 213 // Success. | |
| 214 break; | |
| 215 } | |
| 216 } | |
| 217 } | |
| 218 | |
| 219 TEST_F(EntropyProviderTest, SeededRandGeneratorIsUniform) { | |
| 220 // Verifies. that SeededRandGenerator has a uniform distribution. | |
|
hfung
2012/08/17 17:35:40
extra period?
Alexei Svitkine (slow)
2012/08/17 17:57:40
Done.
| |
| 221 // | |
| 222 // Mirrors RandUtilTest.RandGeneratorIsUniform in base/rand_util_unittest.cc. | |
| 223 | |
| 224 const uint32 kTopOfRange = (std::numeric_limits<uint32>::max() / 4ULL) * 3ULL; | |
| 225 const uint32 kExpectedAverage = kTopOfRange / 2ULL; | |
| 226 const uint32 kAllowedVariance = kExpectedAverage / 50ULL; // +/- 2% | |
| 227 const int kMinAttempts = 1000; | |
| 228 const int kMaxAttempts = 1000000; | |
| 229 | |
| 230 const std::string trial_names[] = { | |
| 231 "TestTrial", | |
| 232 "AnotherTestTrial", | |
| 233 "NewTabButton", | |
| 234 }; | |
| 235 | |
| 236 for (size_t i = 0; i < arraysize(trial_names); ++i) { | |
| 237 const uint32 seed = internal::HashName(trial_names[i]); | |
| 238 internal::SeededRandGenerator rand_generator(seed); | |
| 239 | |
| 240 double cumulative_average = 0.0; | |
| 241 int count = 0; | |
| 242 while (count < kMaxAttempts) { | |
| 243 uint32 value = rand_generator(kTopOfRange); | |
| 244 cumulative_average = (count * cumulative_average + value) / (count + 1); | |
| 245 | |
| 246 // Don't quit too quickly for things to start converging, or we may have | |
| 247 // a false positive. | |
| 248 if (count > kMinAttempts && | |
| 249 kExpectedAverage - kAllowedVariance < cumulative_average && | |
| 250 cumulative_average < kExpectedAverage + kAllowedVariance) { | |
| 251 break; | |
| 252 } | |
| 253 | |
| 254 ++count; | |
| 255 } | |
| 256 | |
| 257 ASSERT_LT(count, kMaxAttempts) << "Expected average was " << | |
| 258 kExpectedAverage << ", average ended at " << cumulative_average; | |
| 259 } | |
| 260 } | |
| 261 | |
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