Fix checkstyle warnings in examples

Change-Id: Iad533f3987792d8713426234f3277c1ef6b72284
Issue-ID: POLICY-1034
Signed-off-by: Dinh Danh Le <dinh.danh.le@ericsson.com>

9 files changed, 124 insertions, 115 deletions

diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
index a044ad14b..2a654c38e 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
@@ -47,15 +47,18 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
 
     /**
      * A map to hold the Anomaly degree/levels/probabilities required for each task.<br>
-     * If there is no task defined for a calculated anomaly-degree, then the default task is used.<br>
-     * The map use (LinkedHashMap) is an insertion-ordered map, so the first interval matching a query is used.
+     * If there is no task defined for a calculated anomaly-degree, then the default task is
+     * used.<br>
+     * The map use (LinkedHashMap) is an insertion-ordered map, so the first interval matching a
+     * query is used.
      */
     // CHECKSTYLE:OFF: checkstyle:magicNumber
     private static final Map<double[], String> TASK_INTERVALS = new LinkedHashMap<>();
+
     static {
-        TASK_INTERVALS.put(new double[] { 0.0, 0.1 }, null); // null will mean default task
-        TASK_INTERVALS.put(new double[] { 0.25, 0.5 }, "AnomalyDetectionDecideTask1");
-        TASK_INTERVALS.put(new double[] { 0.5, 1.01 }, "AnomalyDetectionDecideTask2");
+        TASK_INTERVALS.put(new double[] {0.0, 0.1}, null); // null will mean default task
+        TASK_INTERVALS.put(new double[] {0.25, 0.5}, "AnomalyDetectionDecideTask1");
+        TASK_INTERVALS.put(new double[] {0.5, 1.01}, "AnomalyDetectionDecideTask2");
     }
     // CHECKSTYLE:ON: checkstyle:magicNumber
 
@@ -74,8 +77,8 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
         logger.debug(executor.inFields.toString());
         final double now = (Double) (executor.inFields.get("MonitoredValue"));
         final Integer iteration = (Integer) (executor.inFields.get("Iteration"));
-        final double[] vals = this.forecastingAndAnomaly(now); // double[forecastedValue, AnomalyScore,
-                                                               // AnomalyProbability]
+        // get the double[forecastedValue, AnomalyScore, AnomalyProbability]
+        final double[] vals = this.forecastingAndAnomaly(now);
         final double anomalyness = vals[2];
         String task = null;
         for (final Map.Entry<double[], String> i : TASK_INTERVALS.entrySet()) {
@@ -103,8 +106,8 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
      * Anomaly detection and forecast.
      *
      * @param value The current value
-     * @return Null if the function can not be executed correctly, otherwise double[forecastedValue, AnomalyScore,
-     *         AnomalyProbability]
+     * @return Null if the function can not be executed correctly, otherwise double[forecastedValue,
+     *         AnomalyScore, AnomalyProbability]
      */
     public double[] forecastingAndAnomaly(final double value) {
         try {
@@ -167,7 +170,7 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
         double anomalyProbability = 0.0;
         if (anomalyDetection.getAnomalyScores().size() > 30) {
             // 0.5
-            anomalyProbability = gStatsTest(anomalyDetection.getAnomalyScores(), ANOMALY_SENSITIVITY);
+            anomalyProbability = getStatsTest(anomalyDetection.getAnomalyScores(), ANOMALY_SENSITIVITY);
         }
         // CHECKSTYLE:ON: checkstyle:magicNumber
 
@@ -178,16 +181,16 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
             return null;
         }
 
-        return new double[] { forecastedValue, anomalyScore, anomalyProbability };
+        return new double[] {forecastedValue, anomalyScore, anomalyProbability};
     }
 
     /**
-     * Is the passed value inside the interval, i.e. (value<interval[1] && value>=interval[0])
+     * Is the passed value inside the interval, i.e. (value < interval[1] && value>=interval[0]).
      *
      * @param value The value to check
      * @param interval A 2 element double array describing an interval
-     * @return true if the value is between interval[0] (inclusive) and interval[1] (exclusive), i.e. (value<interval[1]
-     *         && value>=interval[0]). Otherwise false;
+     * @return true if the value is between interval[0] (inclusive) and interval[1] (exclusive),
+     *         i.e. (value < interval[1] && value>=interval[0]). Otherwise false;
      */
     private static boolean checkInterval(final double value, final double[] interval) {
         if (interval == null || interval.length != 2) {
@@ -203,100 +206,89 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
      *
      * @param values the values
      * @param significanceLevel the significance level
-     * @return the double
+     * @return the anomaly probability
      */
-    private static double gStatsTest(final List<Double> values, final double significanceLevel) {
+    private static double getStatsTest(final List<Double> values, final double significanceLevel) {
         if (isAllEqual(values)) {
             return 0.0;
         }
         // the targeted value or the last value
         final double currentV = values.get(values.size() - 1);
-        Double[] lValuesCopy = values.toArray(new Double[values.size()]);
-        Arrays.sort(lValuesCopy); // takes ~40% of method time
-        // if(logger.isDebugEnabled()){
-        // logger.debug("values:" + Arrays.toString(lValuesCopy));
-        // }
+        Double[] lvaluesCopy = values.toArray(new Double[values.size()]);
+        Arrays.sort(lvaluesCopy); // takes ~40% of method time
         // get mean
-        double mean = getMean(lValuesCopy);
-        // get the test value: v
-        double v = getV(lValuesCopy, mean, true);
+        double mean = getMean(lvaluesCopy);
+        // get the test value: val
+        double val = getV(lvaluesCopy, mean, true);
         // get the p value for the test value
-        double pValue = getPValue(lValuesCopy, v, mean); // takes approx 25% of method time
-        // if(logger.isDebugEnabled()){
-        // logger.debug("pValue:" + pValue);
-        // }
+        double pvalue = getPValue(lvaluesCopy, val, mean); // takes approx 25% of method time
 
         // check the critical level
-        while (pValue < significanceLevel) { // takes approx 20% of method time
+        while (pvalue < significanceLevel) { // takes approx 20% of method time
             // the score value as the anomaly probability
-            final double score = (significanceLevel - pValue) / significanceLevel;
-            if (Double.compare(v, currentV) == 0) {
+            final double score = (significanceLevel - pvalue) / significanceLevel;
+            if (Double.compare(val, currentV) == 0) {
                 return score;
             }
             // do the critical check again for the left values
-            lValuesCopy = removevalue(lValuesCopy, v);
-            if (isAllEqual(lValuesCopy)) {
+            lvaluesCopy = removevalue(lvaluesCopy, val);
+            if (isAllEqual(lvaluesCopy)) {
                 return 0.0;
             }
-            // if(logger.isDebugEnabled()){
-            // logger.debug("left values:" + Arrays.toString(lValuesCopy));
-            // }
-            mean = getMean(lValuesCopy);
-            v = getV(lValuesCopy, mean, true);
-            pValue = getPValue(lValuesCopy, v, mean);
+
+            mean = getMean(lvaluesCopy);
+            val = getV(lvaluesCopy, mean, true);
+            pvalue = getPValue(lvaluesCopy, val, mean);
         }
         return 0.0;
     }
 
     /**
-     * get the test value based on mean from sorted values.
+     * Get the test value based on mean from sorted values.
      *
-     * @param lValues the l values
+     * @param lvalues the l values
      * @param mean the mean
      * @param maxValueOnly : only the max extreme value will be tested
      * @return the value to be tested
      */
-    private static double getV(final Double[] lValues, final double mean, final boolean maxValueOnly) {
-        double v = lValues[lValues.length - 1];
+    private static double getV(final Double[] lvalues, final double mean, final boolean maxValueOnly) {
+        double val = lvalues[lvalues.length - 1];
         // max value as the extreme value
         if (maxValueOnly) {
-            return v;
+            return val;
         }
         // check the extreme side
-        if ((v - mean) < (mean - lValues[0])) {
-            v = lValues[0];
+        if ((val - mean) < (mean - lvalues[0])) {
+            val = lvalues[0];
         }
-        return v;
+        return val;
     }
 
     /**
      * calculate the P value for the t distribution.
      *
-     * @param lValues the l values
-     * @param v the v
+     * @param lvalues the l values
+     * @param val the value
      * @param mean the mean
      * @return the p value
      */
-    private static double getPValue(final Double[] lValues, final double v, final double mean) {
+    private static double getPValue(final Double[] lvalues, final double val, final double mean) {
         // calculate z value
-        final double z = FastMath.abs(v - mean) / getStdDev(lValues, mean);
-        // logger.debug("z: " + z);
+        final double z = FastMath.abs(val - mean) / getStdDev(lvalues, mean);
         // calculate T
-        final double n = lValues.length;
+        final double n = lvalues.length;
         final double s = (z * z * n * (2.0 - n)) / (z * z * n - (n - 1.0) * (n - 1.0));
         final double t = FastMath.sqrt(s);
-        // logger.debug("t:" + t);
         // default p value = 0
-        double pValue = 0.0;
+        double pvalue = 0.0;
         if (!Double.isNaN(t)) {
             // t distribution with n-2 degrees of freedom
             final TDistribution tDist = new TDistribution(n - 2);
-            pValue = n * (1.0 - tDist.cumulativeProbability(t));
-            // set max pValue = 1
-            pValue = pValue > 1.0 ? 1.0 : pValue;
+            pvalue = n * (1.0 - tDist.cumulativeProbability(t));
+            // set max pvalue = 1
+            pvalue = pvalue > 1.0 ? 1.0 : pvalue;
         }
-        // logger.debug("value: "+ v + " , pValue: " + pValue);
-        return pValue;
+        return pvalue;
     }
 
     /*
@@ -318,52 +310,52 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
     }
 
     /**
-     * Remove the first occurence of the value v from the array.
+     * Remove the first occurrence of the value val from the array.
      *
-     * @param lValues the l values
-     * @param v the v
+     * @param lvalues the l values
+     * @param val the value
      * @return the double[]
      */
-    private static Double[] removevalue(final Double[] lValues, final double v) {
-        for (int i = 0; i < lValues.length; i++) {
-            if (Double.compare(lValues[i], v) == 0) {
-                final Double[] ret = new Double[lValues.length - 1];
-                System.arraycopy(lValues, 0, ret, 0, i);
-                System.arraycopy(lValues, i + 1, ret, i, lValues.length - i - 1);
+    private static Double[] removevalue(final Double[] lvalues, final double val) {
+        for (int i = 0; i < lvalues.length; i++) {
+            if (Double.compare(lvalues[i], val) == 0) {
+                final Double[] ret = new Double[lvalues.length - 1];
+                System.arraycopy(lvalues, 0, ret, 0, i);
+                System.arraycopy(lvalues, i + 1, ret, i, lvalues.length - i - 1);
                 return ret;
             }
         }
-        return lValues;
+        return lvalues;
     }
 
     /**
      * get mean value of double list.
      *
-     * @param lValues the l values
+     * @param lvalues the l values
      * @return the mean
      */
-    private static double getMean(final Double[] lValues) {
+    private static double getMean(final Double[] lvalues) {
         double sum = 0.0;
-        for (final double d : lValues) {
+        for (final double d : lvalues) {
 
             sum += d;
         }
-        return sum / lValues.length;
+        return sum / lvalues.length;
     }
 
     /**
      * get standard deviation of double list.
      *
-     * @param lValues the l values
+     * @param lvalues the l values
      * @param mean the mean
      * @return stddev
      */
-    private static double getStdDev(final Double[] lValues, final double mean) {
+    private static double getStdDev(final Double[] lvalues, final double mean) {
         double temp = 0.0;
-        for (final double d : lValues) {
+        for (final double d : lvalues) {
             temp += (mean - d) * (mean - d);
         }
-        return FastMath.sqrt(temp / lValues.length);
+        return FastMath.sqrt(temp / lvalues.length);
     }
 
     /**
@@ -383,12 +375,12 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
     /**
      * return true if all values are equal.
      *
-     * @param lValues the l values
+     * @param lvalues the l values
      * @return true, if checks if is all equal
      */
-    private static boolean isAllEqual(final List<Double> lValues) {
-        final double first = lValues.get(0);
-        for (final Double d : lValues) {
+    private static boolean isAllEqual(final List<Double> lvalues) {
+        final double first = lvalues.get(0);
+        for (final Double d : lvalues) {
             if (Double.compare(d, first) != 0) {
                 return false;
             }
@@ -399,12 +391,12 @@ public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
     /**
      * return true if all values are equal.
      *
-     * @param lValues the l values
+     * @param lvalues the l values
      * @return true, if checks if is all equal
      */
-    private static boolean isAllEqual(final Double[] lValues) {
-        final double first = lValues[0];
-        for (final Double d : lValues) {
+    private static boolean isAllEqual(final Double[] lvalues) {
+        final double first = lvalues[0];
+        for (final Double d : lvalues) {
             if (Double.compare(d, first) != 0) {
                 return false;
             }
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDbWrite.java
index 9affa7876..8f54bf12f 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDbWrite.java
@@ -31,10 +31,14 @@ import org.onap.policy.apex.model.basicmodel.dao.DaoParameters;
 import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
 
-public class TestAnomalyDetectionDBWrite {
+public class TestAnomalyDetectionDbWrite {
     private Connection connection;
     TestApexModel<AxPolicyModel> testApexModel;
 
+    /**
+     * Sets up embedded Derby database and the Apex anomaly detection model for the tests.
+     * @throws Exception exception to be thrown while setting up the database connection
+     */
     @Before
     public void setup() throws Exception {
         Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
@@ -50,11 +54,10 @@ public class TestAnomalyDetectionDBWrite {
     }
 
     @Test
-    public void testModelWriteReadJPA() throws Exception {
+    public void testModelWriteReadJpa() throws Exception {
         final DaoParameters DaoParameters = new DaoParameters();
         DaoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
         DaoParameters.setPersistenceUnit("AdaptiveModelsTest");
-
         testApexModel.testApexModelWriteReadJpa(DaoParameters);
     }
 }
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
index 3782f2d88..9e631ef2e 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
@@ -38,6 +38,10 @@ public class TestAnomalyDetectionModel {
     private Connection connection;
     TestApexModel<AxPolicyModel> testApexModel;
 
+    /**
+     * Sets up embedded Derby database and the Apex anomaly detection model for the tests.
+     * @throws Exception exception to be thrown while setting up the database connection
+     */
     @Before
     public void setup() throws Exception {
         Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
@@ -59,17 +63,17 @@ public class TestAnomalyDetectionModel {
     }
 
     @Test
-    public void testModelWriteReadXML() throws Exception {
+    public void testModelWriteReadXml() throws Exception {
         testApexModel.testApexModelWriteReadXml();
     }
 
     @Test
-    public void testModelWriteReadJSON() throws Exception {
+    public void testModelWriteReadJson() throws Exception {
         testApexModel.testApexModelWriteReadJson();
     }
 
     @Test
-    public void testModelWriteReadJPA() throws Exception {
+    public void testModelWriteReadJpa() throws Exception {
         final DaoParameters DaoParameters = new DaoParameters();
         DaoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
         DaoParameters.setPersistenceUnit("AdaptiveModelsTest");
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
index 2b50d69ab..439452cec 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
@@ -25,6 +25,7 @@ import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
 
 /**
+ * The class TestAnomalyDetectionModelCreator.
  * @author Liam Fallon (liam.fallon@ericsson.com)
  */
 public class TestAnomalyDetectionModelCreator implements TestApexModelCreator<AxPolicyModel> {
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTslUseCase.java
index 3d3fad973..1925a53f5 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTslUseCase.java
@@ -56,8 +56,8 @@ import org.slf4j.ext.XLoggerFactory;
  *
  * @author John Keeney (John.Keeney@ericsson.com)
  */
-public class TestAnomalyDetectionTSLUseCase {
-    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAnomalyDetectionTSLUseCase.class);
+public class TestAnomalyDetectionTslUseCase {
+    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAnomalyDetectionTslUseCase.class);
 
     private static final int MAXITERATIONS = 3660;
     private static final Random RAND = new Random(System.currentTimeMillis());
@@ -107,7 +107,7 @@ public class TestAnomalyDetectionTSLUseCase {
 
     @Test
     // once through the long running test below
-    public void TestAnomalyDetectionTSL() throws ApexException, InterruptedException, IOException {
+    public void testAnomalyDetectionTsl() throws ApexException, InterruptedException, IOException {
         final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
         assertNotNull(apexPolicyModel);
 
@@ -156,10 +156,10 @@ public class TestAnomalyDetectionTSLUseCase {
     // Test is disabled by default. uncomment below, or execute using the main() method
     // @Test
     // EG Dos command: apex-core.engine> mvn
-    // -Dtest=org.onap.policy.apex.core.engine.ml.TestAnomalyDetectionTSLUseCase test | findstr /L /C:"Apex [main] DEBUG
+    // -Dtest=org.onap.policy.apex.core.engine.ml.TestAnomalyDetectionTslUseCase test | findstr /L /C:"Apex [main] DEBUG
     // c.e.a.e.TaskSelectionExecutionLogging -
     // TestAnomalyDetectionTSL_Policy0000DecideStateTaskSelectionLogic.getTask():"
-    public void TestAnomalyDetectionTSL_main() throws ApexException, InterruptedException, IOException {
+    public void testAnomalyDetectionTslmain() throws ApexException, InterruptedException, IOException {
 
         final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
         assertNotNull(apexPolicyModel);
@@ -206,6 +206,6 @@ public class TestAnomalyDetectionTSLUseCase {
     }
 
     public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
-        new TestAnomalyDetectionTSLUseCase().TestAnomalyDetectionTSL_main();
+        new TestAnomalyDetectionTslUseCase().testAnomalyDetectionTslmain();
     }
 }
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDbWrite.java
index e096105d9..d4c1ab193 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDbWrite.java
@@ -31,10 +31,14 @@ import org.onap.policy.apex.model.basicmodel.dao.DaoParameters;
 import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
 
-public class TestAutoLearnDBWrite {
+public class TestAutoLearnDbWrite {
     private Connection connection;
     TestApexModel<AxPolicyModel> testApexModel;
 
+    /**
+     * Sets up embedded Derby database and the Apex AutoLearn model for the tests.
+     * @throws Exception exception to be thrown while setting up the database connection
+     */
     @Before
     public void setup() throws Exception {
         Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
@@ -50,7 +54,7 @@ public class TestAutoLearnDBWrite {
     }
 
     @Test
-    public void testModelWriteReadJPA() throws Exception {
+    public void testModelWriteReadJpa() throws Exception {
         final DaoParameters DaoParameters = new DaoParameters();
         DaoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
         DaoParameters.setPersistenceUnit("AdaptiveModelsTest");
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
index beb7a9c80..9bf7ce57a 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
@@ -38,6 +38,10 @@ public class TestAutoLearnModel {
     private Connection connection;
     TestApexModel<AxPolicyModel> testApexModel;
 
+    /**
+     * Sets up embedded Derby database and the Apex AutoLearn model for the tests.
+     * @throws Exception exception to be thrown while setting up the database connection
+     */
     @Before
     public void setup() throws Exception {
         Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
@@ -59,17 +63,17 @@ public class TestAutoLearnModel {
     }
 
     @Test
-    public void testModelWriteReadXML() throws Exception {
+    public void testModelWriteReadXml() throws Exception {
         testApexModel.testApexModelWriteReadXml();
     }
 
     @Test
-    public void testModelWriteReadJSON() throws Exception {
+    public void testModelWriteReadJson() throws Exception {
         testApexModel.testApexModelWriteReadJson();
     }
 
     @Test
-    public void testModelWriteReadJPA() throws Exception {
+    public void testModelWriteReadJpa() throws Exception {
         final DaoParameters DaoParameters = new DaoParameters();
         DaoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
         DaoParameters.setPersistenceUnit("AdaptiveModelsTest");
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
index 11f1991bf..35d049dc7 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
@@ -25,6 +25,7 @@ import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
 
 /**
+ *  The class TestAutoLearnModelCreator.
  * @author Liam Fallon (liam.fallon@ericsson.com)
  */
 public class TestAutoLearnModelCreator implements TestApexModelCreator<AxPolicyModel> {
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTslUseCase.java
index 723b56653..ce9a07e16 100644
--- a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTslUseCase.java
@@ -54,8 +54,8 @@ import org.slf4j.ext.XLoggerFactory;
  *
  * @author John Keeney (John.Keeney@ericsson.com)
  */
-public class TestAutoLearnTSLUseCase {
-    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTSLUseCase.class);
+public class TestAutoLearnTslUseCase {
+    private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTslUseCase.class);
 
     private static final int MAXITERATIONS = 1000;
     private static final Random rand = new Random(System.currentTimeMillis());
@@ -105,7 +105,7 @@ public class TestAutoLearnTSLUseCase {
 
     @Test
     // once through the long running test below
-    public void TestAutoLearnTSL() throws ApexException, InterruptedException, IOException {
+    public void testAutoLearnTsl() throws ApexException, InterruptedException, IOException {
         final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
         assertNotNull(apexPolicyModel);
 
@@ -153,9 +153,9 @@ public class TestAutoLearnTSLUseCase {
      * @throws IOException Signals that an I/O exception has occurred.
      */
     // @Test
-    public void TestAutoLearnTSL_main() throws ApexException, InterruptedException, IOException {
+    public void testAutoLearnTslMain() throws ApexException, InterruptedException, IOException {
 
-        final double WANT = 50.0;
+        final double dwant = 50.0;
         final double toleranceTileJump = 3.0;
 
         final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
@@ -179,10 +179,10 @@ public class TestAutoLearnTSLUseCase {
 
         final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
         assertNotNull(triggerEvent);
-        final double MIN = -100;
-        final double MAX = 100;
+        final double dmin = -100;
+        final double dmax = 100;
 
-        double rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+        double rval = (((rand.nextGaussian() + 1) / 2) * (dmax - dmin)) + dmin;
         triggerEvent.put("MonitoredValue", rval);
         triggerEvent.put("LastMonitoredValue", 0);
 
@@ -207,13 +207,13 @@ public class TestAutoLearnTSLUseCase {
             avcount = Math.min((avcount + 1), 20); // maintain average of only the last 20 values
             avval = ((avval * (avcount - 1)) + val) / (avcount);
 
-            distance = Math.abs(WANT - avval);
+            distance = Math.abs(dwant - avval);
             if (distance < toleranceTileJump) {
-                rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+                rval = (((rand.nextGaussian() + 1) / 2) * (dmax - dmin)) + dmin;
                 val = rval;
                 triggerEvent.put("MonitoredValue", val);
                 LOGGER.info("Iteration " + iteration + ": Average " + avval + " has become closer (" + distance
-                        + ") than " + toleranceTileJump + " to " + WANT + " so reseting val:\t\t\t\t\t\t\t\t" + val);
+                        + ") than " + toleranceTileJump + " to " + dwant + " so reseting val:\t\t\t\t\t\t\t\t" + val);
                 avval = 0;
                 avcount = 0;
             }
@@ -229,6 +229,6 @@ public class TestAutoLearnTSLUseCase {
     }
 
     public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
-        new TestAutoLearnTSLUseCase().TestAutoLearnTSL_main();
+        new TestAutoLearnTslUseCase().testAutoLearnTslMain();
     }
 }