Program: 50997
Project: 50997
Buggy source:
public void run(){
  long tic, toc;
  List<Double> tempLabels=new ArrayList<Double>();
  tempLabels.addAll(labels);
  tic=System.currentTimeMillis();
  SparseVector[] fv=kernel.computeFeatureVectors(dataset,instances,blackList);
  toc=System.currentTimeMillis();
  if (doTFIDF) {
    fv=TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
    fv=KernelUtils.normalize(fv);
  }
  if (doBinary) {
    fv=KernelUtils.convert2BinaryFeatureVectors(fv);
    fv=KernelUtils.normalize(fv);
  }
  List<SparseVector> fvList=Arrays.asList(fv);
  compR.setLabel("String_Node_Str");
  for (int j=0; j < seeds.length; j++) {
    Collections.shuffle(fvList,new Random(seeds[j]));
    Collections.shuffle(tempLabels,new Random(seeds[j]));
    fv=fvList.toArray(new SparseVector[1]);
    double[] target=new double[tempLabels.size()];
    for (int i=0; i < target.length; i++) {
      target[i]=tempLabels.get(i);
    }
    Prediction[] pred=null;
    double[] targetSplit=null;
    if (doCV) {
      pred=LibLINEAR.crossValidate(fv,target,linearParms,linearParms.getNumFolds());
      targetSplit=target;
    }
 else {
      pred=LibLINEAR.trainTestSplit(fv,target,linearParms,linearParms.getSplitFraction());
      targetSplit=LibLINEAR.splitTestTarget(target,linearParms.getSplitFraction());
      if (kernel instanceof RDFWLSubTreeKernel) {
        RDFWLSubTreeKernel k=(RDFWLSubTreeKernel)kernel;
        LibLINEARModel model=LibLINEAR.trainLinearModel(fv,target,linearParms);
        LibLINEARModel.WeightIndexPair[][] fw=model.getFeatureWeights();
        Map<String,String> lm=k.getInverseLabelMap();
        for (        LibLINEARModel.WeightIndexPair[] fwc : fw) {
          Arrays.sort(fwc);
          for (int i=0; i < 10 && i < fwc.length; i++) {
            System.out.print(lm.get(Integer.toString(fwc[i].getIndex())));
            System.out.print("String_Node_Str");
          }
          System.out.println("String_Node_Str");
        }
      }
    }
    for (    EvaluationFunction ef : evalFunctions) {
      resultMap.get(ef)[j]=ef.computeScore(targetSplit,pred);
    }
  }
  double[] comp={toc - tic};
  compR.setScores(comp);
}

Fixed version (ground truth):
public void run(){
  long tic, toc;
  List<Double> tempLabels=new ArrayList<Double>();
  tempLabels.addAll(labels);
  tic=System.currentTimeMillis();
  SparseVector[] fv=kernel.computeFeatureVectors(dataset,instances,blackList);
  toc=System.currentTimeMillis();
  if (doTFIDF) {
    fv=TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
    fv=KernelUtils.normalize(fv);
  }
  if (doBinary) {
    fv=KernelUtils.convert2BinaryFeatureVectors(fv);
    fv=KernelUtils.normalize(fv);
  }
  List<SparseVector> fvList=Arrays.asList(fv);
  compR.setLabel("String_Node_Str");
  for (int j=0; j < seeds.length; j++) {
    Collections.shuffle(fvList,new Random(seeds[j]));
    Collections.shuffle(tempLabels,new Random(seeds[j]));
    fv=fvList.toArray(new SparseVector[1]);
    double[] target=new double[tempLabels.size()];
    for (int i=0; i < target.length; i++) {
      target[i]=tempLabels.get(i);
    }
    Prediction[] pred=null;
    double[] targetSplit=null;
    if (doCV) {
      pred=LibLINEAR.crossValidate(fv,target,linearParms,linearParms.getNumFolds());
      targetSplit=target;
    }
 else {
      pred=LibLINEAR.trainTestSplit(fv,target,linearParms,linearParms.getSplitFraction());
      targetSplit=LibLINEAR.splitTestTarget(target,linearParms.getSplitFraction());
      if (kernel instanceof RDFWLSubTreeKernel) {
        RDFWLSubTreeKernel k=(RDFWLSubTreeKernel)kernel;
        LibLINEARModel model=LibLINEAR.trainLinearModel(fv,target,linearParms);
        LibLINEARModel.WeightIndexPair[][] fw=model.getFeatureWeights();
        Map<String,String> lm=k.getInverseLabelMap();
        System.out.println("String_Node_Str" + lm.size() + "String_Node_Str"+ fw[0].length+ "String_Node_Str"+ fv[0].getLastIndex());
        for (        LibLINEARModel.WeightIndexPair[] fwc : fw) {
          Arrays.sort(fwc);
          for (int i=0; i < 10 && i < fwc.length; i++) {
            System.out.print(lm.get(Integer.toString(fwc[i].getIndex())));
            System.out.print("String_Node_Str");
          }
          System.out.println("String_Node_Str");
        }
      }
    }
    for (    EvaluationFunction ef : evalFunctions) {
      resultMap.get(ef)[j]=ef.computeScore(targetSplit,pred);
    }
  }
  double[] comp={toc - tic};
  compR.setScores(comp);
}