Program: 9828
Project: 9828
Buggy source:
private void addStatsTableRow(){
  double accept=acceptedGlobalStat.getMean() / suggestionListsCount * 100;
  double reject=rejectedGlobalStat.getMean() / suggestionListsCount * 100;
  double fail=failedGlobalStat.getMean() / suggestionListsCount * 100;
  Stat positionStat=new Stat(positionStats);
  double avgPosition=positionStat.getMean();
  if (Double.isNaN(avgPosition)) {
    avgPosition=-1;
  }
  double stdDeviationPosition=positionStat.getStandardDeviation();
  DecimalFormat df=new DecimalFormat("String_Node_Str");
  double additionalInstanceCountEq=new Stat(moreInstancesCountStats).getMean();
  double additionalInstanceCountSC=new Stat(moreInstancesCountStatsSC).getMean();
  double additionalInstanceCount=new Stat(new Stat(moreInstancesCountStats),new Stat(moreInstancesCountStatsSC)).getMean();
  Stat avgSelectedAccuracyEq=new Stat(accSelectedStats);
  Stat avgSelectedAccuracySC=new Stat(accSelectedStatsSC);
  Stat avgSelectedAccuracy=new Stat(avgSelectedAccuracyEq,avgSelectedAccuracySC);
  double avgAccuracy=avgSelectedAccuracy.getMean();
  latexStats.append(logicalAxiomCount + "String_Node_Str" + suggestionListsCount+ "String_Node_Str"+ df.format(accept)+ "String_Node_Str"+ df.format(reject)+ "String_Node_Str"+ df.format(fail)+ "String_Node_Str"+ df.format(avgPosition)+ "String_Node_Str"+ df.format(stdDeviationPosition)+ "String_Node_Str"+ df.format(avgAccuracy * 100)+ "String_Node_Str"+ df.format(additionalInstanceCountEq)+ "String_Node_Str"+ df.format(additionalInstanceCount)+ "String_Node_Str");
}

Fixed version (ground truth):
private void addStatsTableRow(){
  double accept=acceptedGlobalStat.getMean() / suggestionListsCount * 100;
  double reject=rejectedGlobalStat.getMean() / suggestionListsCount * 100;
  double fail=failedGlobalStat.getMean() / suggestionListsCount * 100;
  Stat positionStat=new Stat(positionStats);
  double avgPosition=positionStat.getMean();
  if (Double.isNaN(avgPosition)) {
    avgPosition=-1;
  }
  double stdDeviationPosition=positionStat.getStandardDeviation();
  DecimalFormat df=new DecimalFormat("String_Node_Str");
  double additionalInstanceCountEq=new Stat(moreInstancesCountStats).getMean();
  double additionalInstanceCountSC=new Stat(moreInstancesCountStatsSC).getMean();
  double additionalInstanceCount=new Stat(new Stat(moreInstancesCountStats),new Stat(moreInstancesCountStatsSC)).getMean();
  Stat avgSelectedAccuracyEq=new Stat(accSelectedStats);
  Stat avgSelectedAccuracySC=new Stat(accSelectedStatsSC);
  Stat avgSelectedAccuracy=new Stat(avgSelectedAccuracyEq,avgSelectedAccuracySC);
  double avgAccuracy=avgSelectedAccuracy.getMean();
  latexStats.append(logicalAxiomCount + "String_Node_Str" + suggestionListsCount+ "String_Node_Str"+ df.format(accept)+ "String_Node_Str"+ df.format(reject)+ "String_Node_Str"+ df.format(fail)+ "String_Node_Str"+ df.format(avgPosition)+ "String_Node_Str"+ df.format(stdDeviationPosition)+ "String_Node_Str"+ df.format(avgAccuracy * 100)+ "String_Node_Str"+ df.format(additionalInstanceCountEq)+ "String_Node_Str"+ df.format(additionalInstanceCount)+ "String_Node_Str");
  for (int i=0; i < mat.length; i++) {
    for (int j=0; j < mat[i].length; j++) {
      System.out.print(mat[i][j]);
    }
    System.out.println();
  }
  System.out.println(new FleissKappa().computeKappa(mat));
}