output: #fixed code
                                  @Test
      public void testTrainAndValidate() {
          TestUtils.log(this.getClass(), "testTrainAndValidate");
          RandomValue.setRandomGenerator(new Random(TestConfiguration.RANDOM_SEED));
          DatabaseConfiguration dbConf = TestUtils.getDBConfig();
          
          Dataset[] data = Datasets.carsNumeric(dbConf);
          Dataset trainingData = data[0];
          
          Dataset newData = data[1];
          
          
          
          
          
          
          
          String dbName = "JUnit";
          
          Modeler instance = new Modeler(dbName, dbConf);
          Modeler.TrainingParameters trainingParameters = new Modeler.TrainingParameters();
          trainingParameters.setkFolds(5);
          
          
          //Model Configuration
          
          trainingParameters.setMLmodelClass(MultinomialNaiveBayes.class);
          MultinomialNaiveBayes.TrainingParameters modelTrainingParameters = new MultinomialNaiveBayes.TrainingParameters();
          modelTrainingParameters.setMultiProbabilityWeighted(true);
          trainingParameters.setMLmodelTrainingParameters(modelTrainingParameters);
  
          //data transfomation configuration
          trainingParameters.setDataTransformerClass(DummyXMinMaxNormalizer.class);
          DummyXMinMaxNormalizer.TrainingParameters dtParams = new DummyXMinMaxNormalizer.TrainingParameters();
          trainingParameters.setDataTransformerTrainingParameters(dtParams);
          
          //feature selection configuration
          trainingParameters.setFeatureSelectionClass(null);
          trainingParameters.setFeatureSelectionTrainingParameters(null);
          
          instance.fit(trainingData, trainingParameters);
          
          
          MultinomialNaiveBayes.ValidationMetrics vm = (MultinomialNaiveBayes.ValidationMetrics) instance.validate(trainingData);
          
          double expResult2 = 0.8;
          assertEquals(expResult2, vm.getMacroF1(), TestConfiguration.DOUBLE_ACCURACY_HIGH);
          
          instance = null;
          
          
          TestUtils.log(this.getClass(), "validate");
          
          
          instance = new Modeler(dbName, dbConf);
          
          instance.validate(newData);
          
          
          
          Map<Integer, Object> expResult = new HashMap<>();
          Map<Integer, Object> result = new HashMap<>();
          for(Integer rId : newData) {
              Record r = newData.get(rId);
              expResult.put(rId, r.getY());
              result.put(rId, r.getYPredicted());
          }
          assertEquals(expResult, result);
          
          instance.erase();
      }
input: #vulnerable code 
                                  @Test
      public void testTrainAndValidate() {
          TestUtils.log(this.getClass(), "testTrainAndValidate");
          RandomValue.setRandomGenerator(new Random(TestConfiguration.RANDOM_SEED));
          DatabaseConfiguration dbConf = TestUtils.getDBConfig();
          
          
          Dataset trainingData = new Dataset(dbConf);
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 1.0, 0.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 1.0, 0.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 0.0, 1.0}, 1));
          trainingData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 0.0, 1.0, 0.0, 1.0}, 0));
          trainingData.add(Record.newDataVector(new Double[] {1.0, 0.0, 1.0, 0.0, 0.0, 1.0}, 1));
          
          Dataset newData = new Dataset(dbConf);
          newData.add(Record.newDataVector(new Double[] {0.0, 1.0, 0.0, 1.0, 1.0, 0.0}, 0));
          
          
          
          
          
          
          
          String dbName = "JUnit";
          
          Modeler instance = new Modeler(dbName, dbConf);
          Modeler.TrainingParameters trainingParameters = new Modeler.TrainingParameters();
          trainingParameters.setkFolds(5);
          
          
          //Model Configuration
          
          trainingParameters.setMLmodelClass(MultinomialNaiveBayes.class);
          MultinomialNaiveBayes.TrainingParameters modelTrainingParameters = new MultinomialNaiveBayes.TrainingParameters();
          modelTrainingParameters.setMultiProbabilityWeighted(true);
          trainingParameters.setMLmodelTrainingParameters(modelTrainingParameters);
          
          /*
          trainingParameters.setMLmodelClass(Kmeans.class);
          Kmeans.TrainingParameters modelTrainingParameters = new Kmeans.TrainingParameters();
          modelTrainingParameters.setK(2);
          modelTrainingParameters.setMaxIterations(200);
          modelTrainingParameters.setInitMethod(Kmeans.TrainingParameters.Initialization.FORGY);
          modelTrainingParameters.setDistanceMethod(Kmeans.TrainingParameters.Distance.EUCLIDIAN);
          modelTrainingParameters.setWeighted(false);
          modelTrainingParameters.setCategoricalGamaMultiplier(1.0);
          modelTrainingParameters.setSubsetFurthestFirstcValue(2.0);
          trainingParameters.setMLmodelTrainingParameters(modelTrainingParameters);
          */
  
          //data transfomation configuration
          trainingParameters.setDataTransformerClass(DummyXMinMaxNormalizer.class);
          DummyXMinMaxNormalizer.TrainingParameters dtParams = new DummyXMinMaxNormalizer.TrainingParameters();
          trainingParameters.setDataTransformerTrainingParameters(dtParams);
          
          //feature selection configuration
          trainingParameters.setFeatureSelectionClass(null);
          trainingParameters.setFeatureSelectionTrainingParameters(null);
          
          instance.fit(trainingData, trainingParameters);
          
          
          MultinomialNaiveBayes.ValidationMetrics vm = (MultinomialNaiveBayes.ValidationMetrics) instance.validate(trainingData);
          
          double expResult2 = 0.8;
          assertEquals(expResult2, vm.getMacroF1(), TestConfiguration.DOUBLE_ACCURACY_HIGH);
          
          instance = null;
          
          
          TestUtils.log(this.getClass(), "validate");
          
          
          instance = new Modeler(dbName, dbConf);
          
          instance.validate(newData);
          
          
          
          Map<Integer, Object> expResult = new HashMap<>();
          Map<Integer, Object> result = new HashMap<>();
          for(Integer rId : newData) {
              Record r = newData.get(rId);
              expResult.put(rId, r.getY());
              result.put(rId, r.getYPredicted());
          }
          assertEquals(expResult, result);
          
          instance.erase();
      } 
                              #location 8 
                              #vulnerability type NULL_DEREFERENCE