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Author: mmohan-r7

src/multirefactor/Metrics.java

@@ -1059,12 +1059,13 @@ else if (elementType == 4)
 	}
 
 	// Element recentness objective updated to be normalised as a ratio between 0 and 1.
-	// The original score is divided by the highest value it could be i.e. the accumulative
-	// value that represents if every element were only found in the current version of the project
+	// The original score is divided by the amount of elements to get an average value per element. 
+	// This is then divided by the highest value it could be i.e. the accumulative value that represents if
+	// every element were only found in the current version of the project, in order to get a normalised ratio.
 	public float elementRecentness(ArrayList<List<CompilationUnit>> previousUnits)
 	{
 		int numerator = 0;
-		int denominator = 0;
+		int elements = 0;
 
 		for (Entry<String, Integer> e : this.elementDiversity.entrySet())
 		{			
@@ -1167,12 +1168,13 @@ else if (elementType == 4)
 			}
 
 			numerator += (amount * value);
-			denominator += (previousUnits.size() * value);
+			elements += value;
 		}
 
-		return (float) numerator / (float) denominator;
+		float answer = (float) numerator / (float) elements;
+		return answer / previousUnits.size();
 	}
-		
+	
 	// Returns a value to represent the visibility of a modifier.
 	private float identifier(VisibilityModifier vm)
 	{

src/searches/GeneticAlgorithmSearch.java

@@ -95,7 +95,8 @@ protected RefactoringSequence createInitialSolution(int initialRefactoringRange,
 		ArrayList<String> refactoringInfo = new ArrayList<String>(refactoringAmount);
 		ArrayList<String> affectedClasses = new ArrayList<String>(refactoringAmount);
 		HashMap<String, Integer> elementDiversity = new HashMap<String, Integer>();
-
+		System.out.printf("\r\n Solution %d", solution);
+		
 		for (int j = 0; j < refactoringAmount; j++)
 		{				
 			int[] result = super.randomRefactoring(refactorings);

src/searches/ManyObjectiveSearch.java

@@ -107,7 +107,7 @@ public void run()
 					randomS2 = (int)(Math.random() * population.size());
 				while (randomS2 == randomS1);
 
-				parents[0] = population.get(randomS1);		
+				parents[0] = population.get(randomS1);
 				parents[1] = population.get(randomS2);
 				newGeneration.addAll(crossover(parents[0], parents[1]));
 			}
@@ -231,8 +231,21 @@ private ArrayList<RefactoringSequence> crossover(RefactoringSequence p1, Refacto
 		float finalScore[] = new float[this.c.length];
 
 		// For each refactoring sequence passed in, a cut point is randomly chosen.
-		int cutPoint1 = (Math.random() < 0.5) ? 1 : p1.getRefactorings().size() - 1;
-		int cutPoint2 = (cutPoint1 == 1) ? 1 : p2.getRefactorings().size() - 1;
+		int cutPoint1, cutPoint2;
+		
+		// If the first parent solution has only 1 refactoring, the cut point will be at the end to
+		// avoid the possibility of producing an offspring with no refactorings
+		if (p1.getRefactorings().size() == 1)
+			cutPoint1 = 1;
+		else 
+			cutPoint1 = (Math.random() < 0.5) ? 1 : p1.getRefactorings().size() - 1;
+
+		// If the first parent solution has 2 refactorings, the cut point of the second solution will be
+		// chosen at random between the 2 possibilities. Otherwise, it will be the same as cut point 1.
+		if (p1.getRefactorings().size() == 2)
+			cutPoint2 = (Math.random() < 0.5) ? 1 : p1.getRefactorings().size() - 1;
+		else 
+			cutPoint2 = (cutPoint1 == 1) ? 1 : p2.getRefactorings().size() - 1;
 
 		// Initialise the program model to the original state and generate the first child solution.
 		this.refactorings = super.resetModel(this.refactorings, this.c[0], this.sourceFiles);