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

chrome/common/metrics/entropy_provider_unittest.cc

Index: chrome/common/metrics/entropy_provider_unittest.cc
===================================================================
--- chrome/common/metrics/entropy_provider_unittest.cc	(revision 0)
+++ chrome/common/metrics/entropy_provider_unittest.cc	(revision 0)
@@ -0,0 +1,263 @@
+// 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 <limits>
+
+#include "base/basictypes.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"
+
+class EntropyProviderTest : public testing::Test {
+ public:
+  // 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) {
+    scoped_ptr<SHA1EntropyProvider> sha1_provider(
+        new SHA1EntropyProvider(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) {
+    scoped_ptr<PermutedEntropyProvider> permuted_provider(
+        new PermutedEntropyProvider(entropy_source, entropy_max));
+    return permuted_provider->GetEntropyForTrial(trial_name);
+  }
+};
+
+
+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.
+  ASSERT_NE(trials[0]->group(), trials[1]->group());
+  ASSERT_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.
+  const size_t kMaxEntropySize = (1 << 13);
+  base::FieldTrialList field_trial_list(
+      new PermutedEntropyProvider(1234, kMaxEntropySize));
+  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.
+  ASSERT_NE(trials[0]->group(), trials[1]->group());
+  ASSERT_NE(trials[0]->group_name(), trials[1]->group_name());
+}
+
+TEST_F(EntropyProviderTest, SHA1Entropy) {
+  double results[] = {
+    GenerateSHA1Entropy("hi", "1"),
+    GenerateSHA1Entropy("there", "1"),
+  };
+  ASSERT_NE(results[0], results[1]);
+  for (size_t i = 0; i < arraysize(results); ++i) {
+    ASSERT_LE(0.0, results[i]);
+    ASSERT_GT(1.0, results[i]);
+  }
+
+  ASSERT_EQ(GenerateSHA1Entropy("yo", "1"),
+            GenerateSHA1Entropy("yo", "1"));
+  ASSERT_NE(GenerateSHA1Entropy("yo", "something"),
+            GenerateSHA1Entropy("yo", "else"));
+}
+
+TEST_F(EntropyProviderTest, PermutedEntropy) {
+  const size_t kMaxEntropySize = (1 << 13);
+  double results[] = {
+    GeneratePermutedEntropy(1234, kMaxEntropySize, "1"),
+    GeneratePermutedEntropy(4321, kMaxEntropySize, "1"),
+  };
+  ASSERT_NE(results[0], results[1]);
+  for (size_t i = 0; i < arraysize(results); ++i) {
+    ASSERT_LE(0.0, results[i]);
+    ASSERT_GT(1.0, results[i]);
+  }
+
+  ASSERT_EQ(GeneratePermutedEntropy(1234, kMaxEntropySize, "1"),
+            GeneratePermutedEntropy(1234, kMaxEntropySize, "1"));
+  ASSERT_NE(GeneratePermutedEntropy(1234, kMaxEntropySize, "something"),
+            GeneratePermutedEntropy(1234, kMaxEntropySize, "else"));
+}
+
+TEST_F(EntropyProviderTest, SHA1EntropyIsUniform) {
+  // Choose a random start number but go sequentially from there, so
+  // that each test tries a different range but we never provide uniformly
+  // distributed input data.
+  int current_number = base::RandInt(0, std::numeric_limits<int>::max());
+
+  // The expected value of a random distribution is the average over all
+  // samples as the number of samples approaches infinity.  For a uniform
+  // distribution from [0.0, 1.0) this would be 0.5.
+  //
+  // We do kSamplesBetweenChecks at a time and check if the value has converged
+  // to a narrow interval around 0.5.  A non-uniform distribution would likely
+  // converge at something different, or not converge consistently within this
+  // range (i.e. the test would start timing out occasionally).
+  int kSamplesBetweenChecks = 300;
+  int num_samples = 0;
+  double total_value = 0.0;
+  while (true) {
+    for (int i = 0; i < kSamplesBetweenChecks; ++i) {
+      total_value += GenerateSHA1Entropy(
+          base::IntToString(current_number++), "salt");
+      num_samples++;
+    }
+
+    double average = total_value / num_samples;
+    double kExpectedMin = 0.48;
+    double kExpectedMax = 0.52;
+
+    if (num_samples > 1000 &&
+        (average < kExpectedMin || average > kExpectedMax)) {
+      // Only printed once we have enough samples that it's very unlikely
+      // things haven't converged.
+      printf("After %d samples, the average was %f, outside the expected\n"
+             "range (%f, %f). We will add more samples and check after every\n"
+             "%d samples. If the average does not converge, something\n"
+             "is broken. If it does converge, the test will pass.\n",
+             num_samples, average,
+             kExpectedMin, kExpectedMax, kSamplesBetweenChecks);
+    } else {
+      // Success.
+      break;
+    }
+  }
+}
+
+TEST_F(EntropyProviderTest, PermutedEntropyIsUniform) {
+  // Choose a random start number but go sequentially from there, so
+  // that each test tries a different range but we never provide uniformly
+  // distributed input data.
+  const size_t kMaxEntropySize = (1 << 13);
+  int current_number = base::RandInt(0, kMaxEntropySize - 1);
+
+  // The expected value of a random distribution is the average over all
+  // samples as the number of samples approaches infinity.  For a uniform
+  // distribution from [0.0, 1.0) this would be 0.5.
+  //
+  // We do kSamplesBetweenChecks at a time and check if the value has converged
+  // to a narrow interval around 0.5.  A non-uniform distribution would likely
+  // converge at something different, or not converge consistently within this
+  // range (i.e. the test would start timing out occasionally).
+  int kSamplesBetweenChecks = 300;
+  int num_samples = 0;
+  double total_value = 0.0;
+  while (true) {
+    for (int i = 0; i < kSamplesBetweenChecks; ++i) {
+      total_value += GeneratePermutedEntropy(current_number++ % kMaxEntropySize,
+                                             kMaxEntropySize, "salt");
+      num_samples++;
+    }
+
+    double average = total_value / num_samples;
+    double kExpectedMin = 0.48;
+    double kExpectedMax = 0.52;
+
+    if (num_samples > 1000 &&
+        (average < kExpectedMin || average > kExpectedMax)) {
+      // Only printed once we have enough samples that it's very unlikely
+      // things haven't converged.
+      printf("After %d samples, the average was %f, outside the expected\n"
+             "range (%f, %f). We will add more samples and check after every\n"
+             "%d samples. If the average does not converge, something\n"
+             "is broken. If it does converge, the test will pass.\n",
+             num_samples, average,
+             kExpectedMin, kExpectedMax, kSamplesBetweenChecks);
+    } else {
+      // Success.
+      break;
+    }
+  }
+}
+
+TEST(RandUtilTest, SeededRandGeneratorIsUniform) {
+  // Verifies. that SeededRandGenerator has a uniform distribution.
+  //
+  // Mirrors RandUtilTest.RandGeneratorIsUniform in base/rand_util_unittest.cc.
+
+  const uint64 kTopOfRange = (std::numeric_limits<uint64>::max() / 4ULL) * 3ULL;
+  const uint64 kExpectedAverage = kTopOfRange / 2ULL;
+  const uint64 kAllowedVariance = kExpectedAverage / 50ULL;  // +/- 2%
+  const int kMinAttempts = 1000;
+  const int kMaxAttempts = 1000000;
+
+  const std::string trial_names[] = {
+    "TestTrial",
+    "AnotherTestTrial",
+    "NewTabButton",
+  };
+
+  for (size_t i = 0; i < arraysize(trial_names); ++i) {
+    const uint32 seed = internal::HashName(trial_names[i]);
+    internal::SeededRandGenerator rand_generator(seed);
+
+    double cumulative_average = 0.0;
+    int count = 0;
+    while (count < kMaxAttempts) {
+      uint64 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;
+  }
+}
+