Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/DataGravitation.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/DataGravitation.java	(revision 9)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/DataGravitation.java	(revision 10)
@@ -7,5 +7,9 @@
 import weka.core.Instances;
 
-// TODO comment
+/**
+ * Implements an approach for data weighting suggested after Y. Ma, G. Luo, X. Zeng, and A. Chen: Transfer learning for
+ * cross-company software defect prediction. The instances are weighted higher, the more attributes are within the range they are in the training data. 
+ * @author Steffen Herbold
+ */
 public class DataGravitation implements IProcessesingStrategy, ISetWiseProcessingStrategy {
 
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/InformationGainFilter.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/InformationGainFilter.java	(revision 9)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/InformationGainFilter.java	(revision 10)
@@ -62,8 +62,8 @@
 	
 	/**
-	 * Internal helper function. TODO
-	 * @param testdata
-	 * @param traindata
-	 * @param removeFromTest
+	 * Internal helper function for the application of the filter to both all data set as well as a single data set.
+	 * @param testdata data of the target product
+	 * @param traindata data of the training product
+	 * @param removeFromTest defines whether the attributes shall be removed from the test data as well or not
 	 */
 	private void apply(Instances testdata, Instances traindata, boolean removeFromTest) {
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Undersampling.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Undersampling.java	(revision 9)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Undersampling.java	(revision 10)
@@ -7,5 +7,9 @@
 import weka.filters.supervised.instance.Resample;
 
-// TODO comment
+/**
+ * Implements undersampling, a strategy for handling bias in data. In case there are less positive samples (i.e. defect-prone) samples in the
+ * data than negative samples (i.e. non-defect-prone), the non-defect-prone entities are sampled such thatthe number of defect-prone and non-defect-prone instances is the same afterwards.  
+ * @author Steffen Herbold
+ */
 public class Undersampling implements IProcessesingStrategy,
 		ISetWiseProcessingStrategy {
@@ -21,5 +25,8 @@
 	}
 
-
+	/*
+	 * (non-Javadoc)
+	 * @see de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
+	 */
 	@Override
 	public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
@@ -29,4 +36,8 @@
 	}
 
+	/*
+	 * (non-Javadoc)
+	 * @see de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
+	 */
 	@Override
 	public void apply(Instances testdata, Instances traindata) {
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/BaggingTraining.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/training/BaggingTraining.java	(revision 9)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/training/BaggingTraining.java	(revision 10)
@@ -58,5 +58,5 @@
 		public double classifyInstance(Instance instance) {
 			if( classifiers==null ) {
-				return 0.0; // TODO check how WEKA expects classifyInstance to behave if no classifier exists yet
+				return 0.0;
 			}
 			
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaBaggingTraining2.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaBaggingTraining2.java	(revision 9)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaBaggingTraining2.java	(revision 10)
@@ -67,5 +67,5 @@
 		public double classifyInstance(Instance instance) {
 			if( classifiers==null ) {
-				return 0.0; // TODO check how WEKA expects classifyInstance to behave if no classifier exists yet
+				return 0.0;
 			}