#fixed code
                                public static void main(String[] args) throws ParseException, IOException, TransformationException, AnalysisException, CloneNotSupportedException {
        Options options = new Options();
        options.addOption("under", false, "use undersampling for data selection");
        options.addOption("over", false, "use oversampling for data selection");
        options.addOption("normal", false, "don't use any special strategy for data selection");

        CommandLineParser parser = new GnuParser();
        CommandLine line = parser.parse(options, args);

        if (args.length != 2 ||  !(line.hasOption("under") ^ line.hasOption("over") ^ line.hasOption("normal"))) {
            HelpFormatter formatter = new HelpFormatter();
            formatter.printHelp(" [-options] input-directory", options);
            System.exit(1); 
        }
        String inputDirPath = line.getArgs()[0];
        File inputDirFile = new File(inputDirPath);

        SampleSelector<BxZoneLabel> sampler = null;
        if (line.hasOption("over")) {
            sampler = new OversamplingSelector<BxZoneLabel>(1.0);
        } else if (line.hasOption("under")) {
            sampler = new UndersamplingSelector<BxZoneLabel>(1.0);
        } else if (line.hasOption("normal")) {
            sampler = new NormalSelector<BxZoneLabel>();
        } else {
            System.err.println("Sampling pattern is not specified!");
            System.exit(1);
        }

        List<TrainingSample<BxZoneLabel>> initialTrainingElements = new ArrayList<TrainingSample<BxZoneLabel>>();
        List<TrainingSample<BxZoneLabel>> metaTrainingElements = new ArrayList<TrainingSample<BxZoneLabel>>();
        
        HierarchicalReadingOrderResolver ror = new HierarchicalReadingOrderResolver();
        EvaluationUtils.DocumentsIterator iter = new DocumentsIterator(inputDirPath);
        FeatureVectorBuilder<BxZone, BxPage> vectorBuilder;
        Integer docIdx = 0;
        for(BxDocument doc: iter) {
        	doc = ror.resolve(doc);
        	System.out.println(docIdx + ": " + doc.getFilename());
        	String filename = doc.getFilename();
        	doc = ror.resolve(doc);
        	doc.setFilename(filename);
        	////
        	for (BxZone zone : doc.asZones()) {
        		if (zone.getLabel() != null) {
        			if (zone.getLabel().getCategory() != BxZoneLabelCategory.CAT_METADATA) {
        				zone.setLabel(zone.getLabel().getGeneralLabel());
        			}
        		}
        		else {
        			zone.setLabel(BxZoneLabel.OTH_UNKNOWN);
        		}
        	}
        	vectorBuilder = SVMMetadataZoneClassifier.getFeatureVectorBuilder();
        	List<TrainingSample<BxZoneLabel>> newSamples = BxDocsToTrainingSamplesConverter.getZoneTrainingSamples(doc, vectorBuilder, BxZoneLabel.getIdentityMap());
        	
        	for(TrainingSample<BxZoneLabel> sample: newSamples) {
        		if(sample.getLabel().getCategory() == BxZoneLabelCategory.CAT_METADATA) {
        			metaTrainingElements.add(sample);
        		}
        	}
        	////
        	vectorBuilder = SVMInitialZoneClassifier.getFeatureVectorBuilder();
        	newSamples = BxDocsToTrainingSamplesConverter.getZoneTrainingSamples(doc, vectorBuilder, BxZoneLabel.getLabelToGeneralMap());
        	initialTrainingElements.addAll(newSamples);
        	////
        	++docIdx;
        }

        initialTrainingElements = sampler.pickElements(initialTrainingElements);
        metaTrainingElements = sampler.pickElements(metaTrainingElements);

        toLibSVM(initialTrainingElements, "initial_" + inputDirFile.getName() + ".dat");
        toLibSVM(metaTrainingElements, "meta_" + inputDirFile.getName() + ".dat");
    }
#vulnerable code 
                                public static void main(String[] args) throws ParseException, IOException, TransformationException, AnalysisException {
        Options options = new Options();
        options.addOption("under", false, "use undersampling for data selection");
        options.addOption("over", false, "use oversampling for data selection");
        options.addOption("normal", false, "don't use any special strategy for data selection");

        CommandLineParser parser = new GnuParser();
        CommandLine line = parser.parse(options, args);

        if (args.length != 2 ||  !(line.hasOption("under") ^ line.hasOption("over") ^ line.hasOption("normal"))) {
            HelpFormatter formatter = new HelpFormatter();
            formatter.printHelp(" [-options] input-directory", options);
            System.exit(1); 
        }
        String inputDirPath = line.getArgs()[0];
        File inputDirFile = new File(inputDirPath);

        SampleSelector<BxZoneLabel> sampler = null;
        if (line.hasOption("over")) {
            sampler = new OversamplingSelector<BxZoneLabel>(1.0);
        } else if (line.hasOption("under")) {
            sampler = new UndersamplingSelector<BxZoneLabel>(2.0);
        } else if (line.hasOption("normal")) {
            sampler = new NormalSelector<BxZoneLabel>();
        } else {
            System.err.println("Sampling pattern is not specified!");
            System.exit(1);
        }

        List<TrainingSample<BxZoneLabel>> initialTrainingElements = new ArrayList<TrainingSample<BxZoneLabel>>();
        List<TrainingSample<BxZoneLabel>> metaTrainingElements = new ArrayList<TrainingSample<BxZoneLabel>>();
        
        EvaluationUtils.DocumentsIterator iter = new DocumentsIterator(inputDirPath);
        FeatureVectorBuilder<BxZone, BxPage> vectorBuilder;
        Integer docIdx = 0;
        for(BxDocument doc: iter) {
        	System.out.println(docIdx + ": " + doc.getFilename());
        	////
        	for (BxZone zone : doc.asZones()) {
        		if (zone.getLabel() != null) {
        			if (zone.getLabel().getCategory() != BxZoneLabelCategory.CAT_METADATA) {
        				zone.setLabel(zone.getLabel().getGeneralLabel());
        			}
        		}
        		else {
        			zone.setLabel(BxZoneLabel.OTH_UNKNOWN);
        		}
        	}
        	vectorBuilder = SVMMetadataZoneClassifier.getFeatureVectorBuilder();
        	List<TrainingSample<BxZoneLabel>> newSamples = BxDocsToTrainingSamplesConverter.getZoneTrainingSamples(doc, vectorBuilder, BxZoneLabel.getIdentityMap());
        	
        	for(TrainingSample<BxZoneLabel> sample: newSamples) {
        		if(sample.getLabel().getCategory() == BxZoneLabelCategory.CAT_METADATA) {
        			metaTrainingElements.add(sample);
        		}
        	}
        	////
        	vectorBuilder = SVMInitialZoneClassifier.getFeatureVectorBuilder();
        	newSamples = BxDocsToTrainingSamplesConverter.getZoneTrainingSamples(doc, vectorBuilder, BxZoneLabel.getLabelToGeneralMap());
        	initialTrainingElements.addAll(newSamples);
        	////
        	++docIdx;
        }

        initialTrainingElements = sampler.pickElements(initialTrainingElements);
        metaTrainingElements = sampler.pickElements(metaTrainingElements);

        toLibSVM(initialTrainingElements, "initial_" + inputDirFile.getName() + ".dat");
        toLibSVM(metaTrainingElements, "meta_" + inputDirFile.getName() + ".dat");
    } 
                            #location 37 
                            #vulnerability type NULL_DEREFERENCE