Program: 51024
Project: 51024
Buggy source:
public void run(){
  long tic, toc;
  List<Value> tempLabels=new ArrayList<Value>();
  tempLabels.addAll(labels);
  tic=System.currentTimeMillis();
  SparseVector[] fv=kernel.computeFeatureVectors(dataset,instances,blackList);
  toc=System.currentTimeMillis();
  double[] comp={0.0,0.0};
  comp[0]=toc - tic;
  comp[1]=toc - tic;
  compR.setScores(comp);
  double avg=0;
  for (  SparseVector v : fv) {
    avg+=v.size();
  }
  avg/=fv.length;
  System.out.println("String_Node_Str" + avg);
  double[] accLA=new double[seeds.length];
  double[] f1LA=new double[seeds.length];
  double[] accKA=new double[seeds.length];
  double[] f1KA=new double[seeds.length];
  double[] compLA=new double[seeds.length];
  double[] compKA=new double[seeds.length];
  accL.setLabel("String_Node_Str");
  f1L.setLabel("String_Node_Str");
  accK.setLabel("String_Node_Str");
  f1K.setLabel("String_Node_Str");
  compR.setLabel("String_Node_Str");
  compL.setLabel("String_Node_Str");
  compK.setLabel("String_Node_Str");
  for (int j=0; j < seeds.length; j++) {
    List<SparseVector> fvList=Arrays.asList(fv);
    Collections.shuffle(fvList,new Random(seeds[j]));
    fv=fvList.toArray(new SparseVector[1]);
    Collections.shuffle(tempLabels,new Random(seeds[j]));
    double[] target=LibSVM.createTargets(tempLabels);
    Map<Double,Double> counts=LibSVM.computeClassCounts(target);
    int[] wLabels=new int[counts.size()];
    double[] weights=new double[counts.size()];
    for (    double label : counts.keySet()) {
      wLabels[(int)label - 1]=(int)label;
      weights[(int)label - 1]=1 / counts.get(label);
    }
    svmParms.setWeightLabels(wLabels);
    svmParms.setWeights(weights);
    linearParms.setWeightLabels(wLabels);
    linearParms.setWeights(weights);
    tic=System.currentTimeMillis();
    Prediction[] predA=LibLINEAR.crossValidate(fv,target,linearParms,10);
    toc=System.currentTimeMillis();
    compLA[j]=toc - tic;
    svmParms.setPrecomputedKernel();
    tic=System.currentTimeMillis();
    double[][] matrix=Kernel.featureVectors2Kernel(fv,false);
    Prediction[] predB=LibSVM.crossValidate(matrix,target,svmParms,10);
    toc=System.currentTimeMillis();
    compKA[j]=toc - tic;
    accLA[j]=LibSVM.computeAccuracy(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predA));
    f1LA[j]=LibSVM.computeF1(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predA));
    accKA[j]=LibSVM.computeAccuracy(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predB));
    f1KA[j]=LibSVM.computeF1(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predB));
  }
  accL.setScores(accLA);
  f1L.setScores(f1LA);
  accK.setScores(accKA);
  f1K.setScores(f1KA);
  compL.setScores(compLA);
  compK.setScores(compKA);
}

Fixed version (ground truth):
public void run(){
  long tic, toc;
  List<Value> tempLabels=new ArrayList<Value>();
  tempLabels.addAll(labels);
  tic=System.currentTimeMillis();
  SparseVector[] fv=kernel.computeFeatureVectors(dataset,instances,blackList);
  toc=System.currentTimeMillis();
  double[] comp={0.0,0.0};
  comp[0]=toc - tic;
  comp[1]=toc - tic;
  compR.setScores(comp);
  double avg=0;
  for (  SparseVector v : fv) {
    avg+=v.size();
  }
  avg/=fv.length;
  System.out.println("String_Node_Str" + avg);
  double[] accLA=new double[seeds.length];
  double[] f1LA=new double[seeds.length];
  double[] accKA=new double[seeds.length];
  double[] f1KA=new double[seeds.length];
  double[] compLA=new double[seeds.length];
  double[] compKA=new double[seeds.length];
  accL.setLabel("String_Node_Str");
  f1L.setLabel("String_Node_Str");
  accK.setLabel("String_Node_Str");
  f1K.setLabel("String_Node_Str");
  compR.setLabel("String_Node_Str");
  compL.setLabel("String_Node_Str");
  compK.setLabel("String_Node_Str");
  for (int j=0; j < seeds.length; j++) {
    List<SparseVector> fvList=Arrays.asList(fv);
    Collections.shuffle(fvList,new Random(seeds[j]));
    fv=fvList.toArray(new SparseVector[1]);
    Collections.shuffle(tempLabels,new Random(seeds[j]));
    double[] target=LibSVM.createTargets(tempLabels);
    Map<Double,Double> counts=LibSVM.computeClassCounts(target);
    int[] wLabels=new int[counts.size()];
    double[] weights=new double[counts.size()];
    for (    double label : counts.keySet()) {
      wLabels[(int)label - 1]=(int)label;
      weights[(int)label - 1]=1 / counts.get(label);
    }
    svmParms.setWeightLabels(wLabels);
    svmParms.setWeights(weights);
    linearParms.setWeightLabels(wLabels);
    linearParms.setWeights(weights);
    tic=System.currentTimeMillis();
    Prediction[] predA=LibLINEAR.crossValidate(fv,target,linearParms,10);
    toc=System.currentTimeMillis();
    compLA[j]=toc - tic;
    svmParms.setPrecomputedKernel();
    tic=System.currentTimeMillis();
    double[][] matrix=Kernel.featureVectors2Kernel(fv);
    Prediction[] predB=LibSVM.crossValidate(matrix,target,svmParms,10);
    toc=System.currentTimeMillis();
    compKA[j]=toc - tic;
    accLA[j]=LibSVM.computeAccuracy(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predA));
    f1LA[j]=LibSVM.computeF1(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predA));
    accKA[j]=LibSVM.computeAccuracy(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predB));
    f1KA[j]=LibSVM.computeF1(LibSVM.createTargets(tempLabels),LibSVM.extractLabels(predB));
  }
  accL.setScores(accLA);
  f1L.setScores(f1LA);
  accK.setScores(accKA);
  f1K.setScores(f1KA);
  compL.setScores(compLA);
  compK.setScores(compKA);
}