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diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
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@@ -0,0 +1,119 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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 org.apache.spark.mllib.clustering;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+
+import org.apache.spark.api.java.JavaRDD;
+import scala.Tuple2;
+
+import org.junit.After;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertArrayEquals;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.Vector;
+
+
+public class JavaLDASuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaLDA");
+    ArrayList<Tuple2<Long, Vector>> tinyCorpus = new ArrayList<Tuple2<Long, Vector>>();
+    for (int i = 0; i < LDASuite$.MODULE$.tinyCorpus().length; i++) {
+      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite$.MODULE$.tinyCorpus()[i]._1(),
+          LDASuite$.MODULE$.tinyCorpus()[i]._2()));
+    }
+    JavaRDD<Tuple2<Long, Vector>> tmpCorpus = sc.parallelize(tinyCorpus, 2);
+    corpus = JavaPairRDD.fromJavaRDD(tmpCorpus);
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void localLDAModel() {
+    LocalLDAModel model = new LocalLDAModel(LDASuite$.MODULE$.tinyTopics());
+
+    // Check: basic parameters
+    assertEquals(model.k(), tinyK);
+    assertEquals(model.vocabSize(), tinyVocabSize);
+    assertEquals(model.topicsMatrix(), tinyTopics);
+
+    // Check: describeTopics() with all terms
+    Tuple2<int[], double[]>[] fullTopicSummary = model.describeTopics();
+    assertEquals(fullTopicSummary.length, tinyK);
+    for (int i = 0; i < fullTopicSummary.length; i++) {
+      assertArrayEquals(fullTopicSummary[i]._1(), tinyTopicDescription[i]._1());
+      assertArrayEquals(fullTopicSummary[i]._2(), tinyTopicDescription[i]._2(), 1e-5);
+    }
+  }
+
+  @Test
+  public void distributedLDAModel() {
+    int k = 3;
+    double topicSmoothing = 1.2;
+    double termSmoothing = 1.2;
+
+    // Train a model
+    LDA lda = new LDA();
+    lda.setK(k)
+      .setDocConcentration(topicSmoothing)
+      .setTopicConcentration(termSmoothing)
+      .setMaxIterations(5)
+      .setSeed(12345);
+
+    DistributedLDAModel model = lda.run(corpus);
+
+    // Check: basic parameters
+    LocalLDAModel localModel = model.toLocal();
+    assertEquals(model.k(), k);
+    assertEquals(localModel.k(), k);
+    assertEquals(model.vocabSize(), tinyVocabSize);
+    assertEquals(localModel.vocabSize(), tinyVocabSize);
+    assertEquals(model.topicsMatrix(), localModel.topicsMatrix());
+
+    // Check: topic summaries
+    Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics();
+    assertEquals(roundedTopicSummary.length, k);
+    Tuple2<int[], double[]>[] roundedLocalTopicSummary = localModel.describeTopics();
+    assertEquals(roundedLocalTopicSummary.length, k);
+
+    // Check: log probabilities
+    assert(model.logLikelihood() < 0.0);
+    assert(model.logPrior() < 0.0);
+  }
+
+  private static int tinyK = LDASuite$.MODULE$.tinyK();
+  private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
+  private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
+  private static Tuple2<int[], double[]>[] tinyTopicDescription =
+      LDASuite$.MODULE$.tinyTopicDescription();
+  JavaPairRDD<Long, Vector> corpus;
+
+}