#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
#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();
    }