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diff --git a/impl_core/src/test/java/io/opencensus/implcore/stats/MutableAggregationTest.java b/impl_core/src/test/java/io/opencensus/implcore/stats/MutableAggregationTest.java
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+/*
+ * Copyright 2017, OpenCensus Authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package io.opencensus.implcore.stats;
+
+import static com.google.common.truth.Truth.assertThat;
+import static io.opencensus.implcore.stats.StatsTestUtil.assertAggregationDataEquals;
+
+import com.google.common.collect.ImmutableList;
+import io.opencensus.common.Timestamp;
+import io.opencensus.implcore.stats.MutableAggregation.MutableCount;
+import io.opencensus.implcore.stats.MutableAggregation.MutableDistribution;
+import io.opencensus.implcore.stats.MutableAggregation.MutableLastValueDouble;
+import io.opencensus.implcore.stats.MutableAggregation.MutableLastValueLong;
+import io.opencensus.implcore.stats.MutableAggregation.MutableMean;
+import io.opencensus.implcore.stats.MutableAggregation.MutableSumDouble;
+import io.opencensus.implcore.stats.MutableAggregation.MutableSumLong;
+import io.opencensus.metrics.export.Distribution;
+import io.opencensus.metrics.export.Distribution.Bucket;
+import io.opencensus.metrics.export.Distribution.BucketOptions;
+import io.opencensus.metrics.export.Point;
+import io.opencensus.metrics.export.Value;
+import io.opencensus.stats.AggregationData;
+import io.opencensus.stats.AggregationData.CountData;
+import io.opencensus.stats.AggregationData.DistributionData;
+import io.opencensus.stats.AggregationData.DistributionData.Exemplar;
+import io.opencensus.stats.AggregationData.LastValueDataDouble;
+import io.opencensus.stats.AggregationData.LastValueDataLong;
+import io.opencensus.stats.AggregationData.MeanData;
+import io.opencensus.stats.AggregationData.SumDataDouble;
+import io.opencensus.stats.AggregationData.SumDataLong;
+import io.opencensus.stats.BucketBoundaries;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Unit tests for {@link io.opencensus.implcore.stats.MutableAggregation}. */
+@RunWith(JUnit4.class)
+public class MutableAggregationTest {
+
+  @Rule public ExpectedException thrown = ExpectedException.none();
+
+  private static final double TOLERANCE = 1e-6;
+  private static final BucketBoundaries BUCKET_BOUNDARIES =
+      BucketBoundaries.create(Arrays.asList(-10.0, 0.0, 10.0));
+  private static final BucketBoundaries BUCKET_BOUNDARIES_EMPTY =
+      BucketBoundaries.create(Collections.<Double>emptyList());
+  private static final Timestamp TIMESTAMP = Timestamp.create(60, 0);
+
+  @Test
+  public void testCreateEmpty() {
+    assertThat(MutableSumDouble.create().getSum()).isWithin(TOLERANCE).of(0);
+    assertThat(MutableSumLong.create().getSum()).isWithin(TOLERANCE).of(0);
+    assertThat(MutableCount.create().getCount()).isEqualTo(0);
+    assertThat(MutableMean.create().getMean()).isWithin(TOLERANCE).of(0);
+    assertThat(MutableLastValueDouble.create().getLastValue()).isNaN();
+    assertThat(MutableLastValueLong.create().getLastValue()).isNaN();
+
+    BucketBoundaries bucketBoundaries = BucketBoundaries.create(Arrays.asList(0.1, 2.2, 33.3));
+    MutableDistribution mutableDistribution = MutableDistribution.create(bucketBoundaries);
+    assertThat(mutableDistribution.getMean()).isWithin(TOLERANCE).of(0);
+    assertThat(mutableDistribution.getCount()).isEqualTo(0);
+    assertThat(mutableDistribution.getMin()).isPositiveInfinity();
+    assertThat(mutableDistribution.getMax()).isNegativeInfinity();
+    assertThat(mutableDistribution.getSumOfSquaredDeviations()).isWithin(TOLERANCE).of(0);
+    assertThat(mutableDistribution.getBucketCounts()).isEqualTo(new long[4]);
+    assertThat(mutableDistribution.getExemplars()).isEqualTo(new Exemplar[4]);
+
+    MutableDistribution mutableDistributionNoHistogram =
+        MutableDistribution.create(BUCKET_BOUNDARIES_EMPTY);
+    assertThat(mutableDistributionNoHistogram.getExemplars()).isNull();
+  }
+
+  @Test
+  public void testNullBucketBoundaries() {
+    thrown.expect(NullPointerException.class);
+    thrown.expectMessage("bucketBoundaries should not be null.");
+    MutableDistribution.create(null);
+  }
+
+  @Test
+  public void testNoBoundaries() {
+    List<Double> buckets = Arrays.asList();
+    MutableDistribution noBoundaries = MutableDistribution.create(BucketBoundaries.create(buckets));
+    assertThat(noBoundaries.getBucketCounts().length).isEqualTo(1);
+    assertThat(noBoundaries.getBucketCounts()[0]).isEqualTo(0);
+  }
+
+  @Test
+  public void testAdd() {
+    List<MutableAggregation> aggregations =
+        Arrays.asList(
+            MutableSumDouble.create(),
+            MutableSumLong.create(),
+            MutableCount.create(),
+            MutableMean.create(),
+            MutableDistribution.create(BUCKET_BOUNDARIES),
+            MutableLastValueDouble.create(),
+            MutableLastValueLong.create());
+
+    List<Double> values = Arrays.asList(-1.0, 1.0, -5.0, 20.0, 5.0);
+
+    for (double value : values) {
+      for (MutableAggregation aggregation : aggregations) {
+        aggregation.add(value, Collections.<String, String>emptyMap(), TIMESTAMP);
+      }
+    }
+
+    assertAggregationDataEquals(
+        aggregations.get(0).toAggregationData(),
+        AggregationData.SumDataDouble.create(20.0),
+        TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(1).toAggregationData(), AggregationData.SumDataLong.create(20), TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(2).toAggregationData(), AggregationData.CountData.create(5), TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(3).toAggregationData(),
+        AggregationData.MeanData.create(4.0, 5),
+        TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(4).toAggregationData(),
+        AggregationData.DistributionData.create(
+            4.0, 5, -5.0, 20.0, 372, Arrays.asList(0L, 2L, 2L, 1L)),
+        TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(5).toAggregationData(),
+        AggregationData.LastValueDataDouble.create(5.0),
+        TOLERANCE);
+    assertAggregationDataEquals(
+        aggregations.get(6).toAggregationData(),
+        AggregationData.LastValueDataLong.create(5),
+        TOLERANCE);
+  }
+
+  @Test
+  public void testAdd_DistributionWithExemplarAttachments() {
+    MutableDistribution mutableDistribution = MutableDistribution.create(BUCKET_BOUNDARIES);
+    MutableDistribution mutableDistributionNoHistogram =
+        MutableDistribution.create(BUCKET_BOUNDARIES_EMPTY);
+    List<Double> values = Arrays.asList(-1.0, 1.0, -5.0, 20.0, 5.0);
+    List<Map<String, String>> attachmentsList =
+        ImmutableList.<Map<String, String>>of(
+            Collections.<String, String>singletonMap("k1", "v1"),
+            Collections.<String, String>singletonMap("k2", "v2"),
+            Collections.<String, String>singletonMap("k3", "v3"),
+            Collections.<String, String>singletonMap("k4", "v4"),
+            Collections.<String, String>singletonMap("k5", "v5"));
+    List<Timestamp> timestamps =
+        Arrays.asList(
+            Timestamp.fromMillis(500),
+            Timestamp.fromMillis(1000),
+            Timestamp.fromMillis(2000),
+            Timestamp.fromMillis(3000),
+            Timestamp.fromMillis(4000));
+    for (int i = 0; i < values.size(); i++) {
+      mutableDistribution.add(values.get(i), attachmentsList.get(i), timestamps.get(i));
+      mutableDistributionNoHistogram.add(values.get(i), attachmentsList.get(i), timestamps.get(i));
+    }
+
+    // Each bucket can only have up to one exemplar. If there are more than one exemplars in a
+    // bucket, only the last one will be kept.
+    List<Exemplar> expected =
+        Arrays.<Exemplar>asList(
+            null,
+            Exemplar.create(values.get(2), timestamps.get(2), attachmentsList.get(2)),
+            Exemplar.create(values.get(4), timestamps.get(4), attachmentsList.get(4)),
+            Exemplar.create(values.get(3), timestamps.get(3), attachmentsList.get(3)));
+    assertThat(mutableDistribution.getExemplars())
+        .asList()
+        .containsExactlyElementsIn(expected)
+        .inOrder();
+    assertThat(mutableDistributionNoHistogram.getExemplars()).isNull();
+  }
+
+  @Test
+  public void testCombine_SumCountMean() {
+    // combine() for Mutable Sum, Count and Mean will pick up fractional stats
+    List<MutableAggregation> aggregations1 =
+        Arrays.asList(
+            MutableSumDouble.create(),
+            MutableSumLong.create(),
+            MutableCount.create(),
+            MutableMean.create());
+    List<MutableAggregation> aggregations2 =
+        Arrays.asList(
+            MutableSumDouble.create(),
+            MutableSumLong.create(),
+            MutableCount.create(),
+            MutableMean.create());
+
+    for (double val : Arrays.asList(-1.0, -5.0)) {
+      for (MutableAggregation aggregation : aggregations1) {
+        aggregation.add(val, Collections.<String, String>emptyMap(), TIMESTAMP);
+      }
+    }
+    for (double val : Arrays.asList(10.0, 50.0)) {
+      for (MutableAggregation aggregation : aggregations2) {
+        aggregation.add(val, Collections.<String, String>emptyMap(), TIMESTAMP);
+      }
+    }
+
+    List<MutableAggregation> combined =
+        Arrays.asList(
+            MutableSumDouble.create(),
+            MutableSumLong.create(),
+            MutableCount.create(),
+            MutableMean.create());
+    double fraction1 = 1.0;
+    double fraction2 = 0.6;
+    for (int i = 0; i < combined.size(); i++) {
+      combined.get(i).combine(aggregations1.get(i), fraction1);
+      combined.get(i).combine(aggregations2.get(i), fraction2);
+    }
+
+    assertThat(((MutableSumDouble) combined.get(0)).getSum()).isWithin(TOLERANCE).of(30);
+    assertThat(((MutableSumLong) combined.get(1)).getSum()).isWithin(TOLERANCE).of(30);
+    assertThat(((MutableCount) combined.get(2)).getCount()).isEqualTo(3);
+    assertThat(((MutableMean) combined.get(3)).getMean()).isWithin(TOLERANCE).of(10);
+  }
+
+  @Test
+  public void testCombine_Distribution() {
+    // combine() for Mutable Distribution will ignore fractional stats
+    MutableDistribution distribution1 = MutableDistribution.create(BUCKET_BOUNDARIES);
+    MutableDistribution distribution2 = MutableDistribution.create(BUCKET_BOUNDARIES);
+    MutableDistribution distribution3 = MutableDistribution.create(BUCKET_BOUNDARIES);
+
+    for (double val : Arrays.asList(5.0, -5.0)) {
+      distribution1.add(val, Collections.<String, String>emptyMap(), TIMESTAMP);
+    }
+    for (double val : Arrays.asList(10.0, 20.0)) {
+      distribution2.add(val, Collections.<String, String>emptyMap(), TIMESTAMP);
+    }
+    for (double val : Arrays.asList(-10.0, 15.0, -15.0, -20.0)) {
+      distribution3.add(val, Collections.<String, String>emptyMap(), TIMESTAMP);
+    }
+
+    MutableDistribution combined = MutableDistribution.create(BUCKET_BOUNDARIES);
+    combined.combine(distribution1, 1.0); // distribution1 will be combined
+    combined.combine(distribution2, 0.6); // distribution2 will be ignored
+    verifyMutableDistribution(combined, 0, 2, -5, 5, 50.0, new long[] {0, 1, 1, 0}, TOLERANCE);
+
+    combined.combine(distribution2, 1.0); // distribution2 will be combined
+    verifyMutableDistribution(combined, 7.5, 4, -5, 20, 325.0, new long[] {0, 1, 1, 2}, TOLERANCE);
+
+    combined.combine(distribution3, 1.0); // distribution3 will be combined
+    verifyMutableDistribution(combined, 0, 8, -20, 20, 1500.0, new long[] {2, 2, 1, 3}, TOLERANCE);
+  }
+
+  @Test
+  public void mutableAggregation_ToAggregationData() {
+    assertThat(MutableSumDouble.create().toAggregationData()).isEqualTo(SumDataDouble.create(0));
+    assertThat(MutableSumLong.create().toAggregationData()).isEqualTo(SumDataLong.create(0));
+    assertThat(MutableCount.create().toAggregationData()).isEqualTo(CountData.create(0));
+    assertThat(MutableMean.create().toAggregationData()).isEqualTo(MeanData.create(0, 0));
+    assertThat(MutableDistribution.create(BUCKET_BOUNDARIES).toAggregationData())
+        .isEqualTo(
+            DistributionData.create(
+                0,
+                0,
+                Double.POSITIVE_INFINITY,
+                Double.NEGATIVE_INFINITY,
+                0,
+                Arrays.asList(0L, 0L, 0L, 0L)));
+    assertThat(MutableLastValueDouble.create().toAggregationData())
+        .isEqualTo(LastValueDataDouble.create(Double.NaN));
+    assertThat(MutableLastValueLong.create().toAggregationData())
+        .isEqualTo(LastValueDataLong.create(0));
+  }
+
+  @Test
+  public void mutableAggregation_ToPoint() {
+    assertThat(MutableSumDouble.create().toPoint(TIMESTAMP))
+        .isEqualTo(Point.create(Value.doubleValue(0), TIMESTAMP));
+    assertThat(MutableSumLong.create().toPoint(TIMESTAMP))
+        .isEqualTo(Point.create(Value.longValue(0), TIMESTAMP));
+    assertThat(MutableCount.create().toPoint(TIMESTAMP))
+        .isEqualTo(Point.create(Value.longValue(0), TIMESTAMP));
+    assertThat(MutableMean.create().toPoint(TIMESTAMP))
+        .isEqualTo(Point.create(Value.doubleValue(0), TIMESTAMP));
+
+    thrown.expect(IllegalArgumentException.class);
+    thrown.expectMessage("bucket boundary should be > 0");
+    assertThat(MutableDistribution.create(BUCKET_BOUNDARIES).toPoint(TIMESTAMP))
+        .isEqualTo(
+            Point.create(
+                Value.distributionValue(
+                    Distribution.create(
+                        0,
+                        0,
+                        0,
+                        BucketOptions.explicitOptions(BUCKET_BOUNDARIES.getBoundaries()),
+                        Arrays.asList(
+                            Bucket.create(0),
+                            Bucket.create(0),
+                            Bucket.create(0),
+                            Bucket.create(0)))),
+                TIMESTAMP));
+  }
+
+  private static void verifyMutableDistribution(
+      MutableDistribution mutableDistribution,
+      double mean,
+      long count,
+      double min,
+      double max,
+      double sumOfSquaredDeviations,
+      long[] bucketCounts,
+      double tolerance) {
+    assertThat(mutableDistribution.getMean()).isWithin(tolerance).of(mean);
+    assertThat(mutableDistribution.getCount()).isEqualTo(count);
+    assertThat(mutableDistribution.getMin()).isWithin(tolerance).of(min);
+    assertThat(mutableDistribution.getMax()).isWithin(tolerance).of(max);
+    assertThat(mutableDistribution.getSumOfSquaredDeviations())
+        .isWithin(tolerance)
+        .of(sumOfSquaredDeviations);
+    assertThat(mutableDistribution.getBucketCounts()).isEqualTo(bucketCounts);
+  }
+}