Program: 144080
Project: 144080
Buggy source:
private void sampleZ(int iterationNumber){
  for (int p=0; p < numberOfDomains; p++) {
    for (int j=0; j < numberOfFunctions; j++) {
      dp[j].remove_topic_assignment(zSamples[iterationNumber][p][j]);
      int total_cnt=dp[j].prob_topics();
      double z_probabilities[]=new double[total_cnt];
      for (int i=0; i < total_cnt; i++) {
        z_probabilities[i]=Math.log(dp[j].pdf[i].prob);
      }
      disagreements[j][p]=0;
      for (int i=0; i < numberOfDataSamples[p]; i++)       if (functionOutputsArray[j][p][i] != labelsSamples[iterationNumber][p][i])       disagreements[j][p]++;
      for (int i=0; i < total_cnt - 1; i++) {
        int k=dp[j].pdf[i].topic;
        z_probabilities[i]+=disagreements[j][p] * Math.log(errorRateSamples[iterationNumber + 1][k][j]);
        z_probabilities[i]+=(numberOfDataSamples[p] - disagreements[j][p]) * Math.log(1 - errorRateSamples[iterationNumber + 1][k][j]);
      }
      z_probabilities[total_cnt - 1]+=logBeta(alpha_e + disagreements[j][p],beta_e + numberOfDataSamples[p] - disagreements[j][p]) - logBeta(alpha_e,beta_e);
      for (int i=1; i < total_cnt; i++) {
        z_probabilities[i]=MatrixUtilities.computeLogSumExp(z_probabilities[i - 1],z_probabilities[i]);
      }
      double uniform=Math.log(random.nextDouble()) + z_probabilities[total_cnt - 1];
      zSamples[iterationNumber + 1][p][j]=dp[j].pdf[total_cnt - 1].topic;
      for (int i=0; i < total_cnt - 1; i++) {
        if (z_probabilities[i] > uniform) {
          zSamples[iterationNumber + 1][p][j]=dp[j].pdf[i].topic;
          sum_1[j][dp[j].pdf[i].topic]+=numberOfDataSamples[p];
          sum_2[j][dp[j].pdf[i].topic]+=disagreements[j][p];
          dp[j].add_topic_assingment(dp[j].pdf[i].topic);
          break;
        }
      }
      if (zSamples[iterationNumber + 1][p][j] == dp[j].pdf[total_cnt - 1].topic) {
        sum_1[j][dp[j].pdf[total_cnt - 1].topic]+=numberOfDataSamples[p];
        sum_2[j][dp[j].pdf[total_cnt - 1].topic]+=disagreements[j][p];
        dp[j].add_topic_assingment(dp[j].pdf[total_cnt - 1].topic);
        int disagreementCount=0;
        for (int i=0; i < numberOfDataSamples[p]; i++)         if (functionOutputsArray[j][p][i] != labelsSamples[iterationNumber][p][i])         disagreementCount++;
        errorRateSamples[iterationNumber + 1][dp[j].pdf[total_cnt - 1].topic][j]=randomDataGenerator.nextBeta(alpha_e + disagreementCount,beta_e + numberOfDataSamples[p] - disagreementCount);
      }
    }
  }
}

Fixed version (ground truth):
private void sampleZ(int iterationNumber){
  for (int p=0; p < numberOfDomains; p++) {
    for (int j=0; j < numberOfFunctions; j++) {
      dp[j].remove_topic_assignment(zSamples[iterationNumber][p][j]);
      int total_cnt=dp[j].prob_topics();
      double z_probabilities[]=new double[total_cnt];
      for (int i=0; i < total_cnt; i++) {
        z_probabilities[i]=Math.log(dp[j].pdf[i].prob);
      }
      disagreements[j][p]=0;
      for (int i=0; i < numberOfDataSamples[p]; i++)       if (functionOutputsArray[j][p][i] != labelsSamples[iterationNumber][p][i])       disagreements[j][p]++;
      for (int i=0; i < total_cnt - 1; i++) {
        int k=dp[j].pdf[i].topic;
        z_probabilities[i]+=disagreements[j][p] * Math.log(errorRateSamples[iterationNumber + 1][k][j]);
        z_probabilities[i]+=(numberOfDataSamples[p] - disagreements[j][p]) * Math.log(1 - errorRateSamples[iterationNumber + 1][k][j]);
      }
      z_probabilities[total_cnt - 1]+=logBeta(alpha_e + disagreements[j][p],beta_e + numberOfDataSamples[p] - disagreements[j][p]) - logBeta(alpha_e,beta_e);
      for (int i=1; i < total_cnt; i++) {
        z_probabilities[i]=MatrixUtilities.computeLogSumExp(z_probabilities[i - 1],z_probabilities[i]);
      }
      double uniform=Math.log(random.nextDouble()) + z_probabilities[total_cnt - 1];
      zSamples[iterationNumber + 1][p][j]=dp[j].pdf[total_cnt - 1].topic;
      for (int i=0; i < total_cnt - 1; i++) {
        if (z_probabilities[i] > uniform) {
          zSamples[iterationNumber + 1][p][j]=dp[j].pdf[i].topic;
          sum_1[j][dp[j].pdf[i].topic]+=numberOfDataSamples[p];
          sum_2[j][dp[j].pdf[i].topic]+=disagreements[j][p];
          dp[j].add_topic_assingment(dp[j].pdf[i].topic);
          break;
        }
      }
      if (zSamples[iterationNumber + 1][p][j] == dp[j].pdf[total_cnt - 1].topic) {
        sum_1[j][dp[j].pdf[total_cnt - 1].topic]+=numberOfDataSamples[p];
        sum_2[j][dp[j].pdf[total_cnt - 1].topic]+=disagreements[j][p];
        dp[j].add_topic_assingment(dp[j].pdf[total_cnt - 1].topic);
        errorRateSamples[iterationNumber + 1][dp[j].pdf[total_cnt - 1].topic][j]=randomDataGenerator.nextBeta(alpha_e + disagreements[j][p],beta_e + numberOfDataSamples[p] - disagreements[j][p]);
      }
    }
  }
}