#fixed code
                                @Override
    protected void predictDataset(Dataframe newData) {
        _predictDataset(newData, false);
    }
#vulnerable code 
                                @Override
    protected void predictDataset(Dataframe newData) {
        Map<List<Object>, Double> similarities = knowledgeBase.getModelParameters().getSimilarities();
        
        //generate recommendation for each record in the list
        for(Map.Entry<Integer, Record> e : newData.entries()) {
            Integer rId = e.getKey();
            Record r = e.getValue();
            Map<Object, Object> recommendations = new HashMap<>();
            
            Map<Object, Double> simSums = new HashMap<>();
            for(Map.Entry<Object, Object> entry : r.getX().entrySet()) {
                Object row = entry.getKey();
                Double score = TypeInference.toDouble(entry.getValue());
                
                //Since we can't use 2D Maps due to mongo, we are forced to loop
                //the whole similarities map which is very inefficient.
                for(Map.Entry<List<Object>, Double> entry2 : similarities.entrySet()) {
                    List<Object> tpk = entry2.getKey();
                    if(!tpk.get(0).equals(row)) {
                        continue; //filter the irrelevant two pair key combinations that do not include the row
                    }
                    
                    Object column = tpk.get(1);
                    if(r.getX().containsKey(column)) {
                        continue; // they already rated this
                    }
                    
                    Double previousRecValue = TypeInference.toDouble(recommendations.get(column));
                    Double previousSimsumValue = simSums.get(column);
                    if(previousRecValue==null) {
                        previousRecValue=0.0;
                        previousSimsumValue=0.0;
                    }
                    
                    Double similarity = entry2.getValue();
                    
                    recommendations.put(column, previousRecValue+similarity*score);
                    simSums.put(column, previousSimsumValue+similarity);
                }
            }
            
            for(Map.Entry<Object, Object> entry : recommendations.entrySet()) {
                Object column = entry.getKey();
                Double score = TypeInference.toDouble(entry.getValue());
                
                recommendations.put(column, score/simSums.get(column));
            }
            simSums = null;
            
            if(!recommendations.isEmpty()) {
                //sort recommendation by popularity
                recommendations = MapFunctions.sortNumberMapByValueDescending(recommendations);
                newData._unsafe_set(rId, new Record(r.getX(), r.getY(), recommendations.keySet().iterator().next(), new AssociativeArray(recommendations)));
            }
        }
    } 
                            #location 12 
                            #vulnerability type NULL_DEREFERENCE