I have troubles to run Binary code similarity measures by following the README instructions:

Binary code similarity measures
1. Download dataset                                                                                      
    . Download POJ-104 datasets from here and extract them into data/.

2. Compile and preprocess                                                                         
    Run python extract_obj.py -a data/obj (clang++-3.7.1 required)`

I downloaded the datasets and put it under nberf/data. Since there is no extract_obj.py under nberf, I changed the command to

  (py3_env): python preprocess/extract_obj.py -a data/obj` 

I got

usage: extract_obj.py [-h] [--poj-dir POJ_DIR] [--filter-list FILTER_LIST] [--asm_type {x86,mips}] [--output-asm-dir OUTPUT_ASM_DIR]
                                   [--num-workers NUM_WORKERS]
extract_obj.py: error: ambiguous option: -a could match -asm_t, -asm  

Look at the parse_args in extract_obj.py, there is no '-a' argument, it seems to be a typo of -asm. If I copy data to preprocessing and run the command:

  (py3_env) nberf: cp -r data preprocess/data
  (py3_env) nberf: cd preprocess
  (py3_env) nberf/preprocess: python extract_obj.py -asm data/obj` 

now I got

Traceback (most recent call last):
  File "extract_obj.py", line 218, in <module>
    main()
  File "extract_obj.py", line 181, in main
    with open(args.filter_list) as f:
FileNotFoundError: [Errno 2] No such file or directory: './preprocess/filter_list.txt'

My env

Ubuntu 20.04.2 LTS
Python 3.8.5

Any steps I misunderstood or missed? Any help is greatly appreciated.

Dennis

$ python preprocess/vul_preprocess.py ... FileNotFoundError: [Errno 2] No such file or directory: 'data/vul/function.csv'

Can you please add the csv file to the vulnerability data that can be downloaded directly?

How much GPU memory is required to run the decompiler? When I run on GPU with 11GB of memory I get out-of-error messages like:

File "/data/pfps/nbref/baseline_model/modules/encoder_decoder_layers.py", line 108, in forward
    energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale.cuda()
RuntimeError: CUDA out of memory. Tried to allocate 60.00 MiB (GPU 0; 10.92 GiB total capacity; 10.15 GiB already allocated; 10.6\
9 MiB free; 10.32 GiB reserved in total by PyTorch)

Here's an error when trying to run python run_tree_transformer.py

  File "run_tree_transformer.py", line 236, in <module>
    main()
  File "run_tree_transformer.py", line 212, in main
    , args.device, criterion, max_len_trg, train_flag=True)
  File "~/nbref/baseline_model/data_utils/train_tree_encoder.py", line 346, in train_eval_tree
    trg_in = model.gnn(batch_graph)
  File "~/nbref/venv/lib64/python3/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "~/nbref/baseline_model/modules/transformer_tree_model.py", line 144, in forward
    merged = _merge_on(graphs, 'nodes', 'h')
  File "~/nbref/baseline_model/modules/encoder_decoder_layers.py", line 36, in _merge_on
    feat = Fdgl.pad_packed_tensor(feat, batch_num_objs, 0 )
  File "~/nbref/venv/lib64/python3/site-packages/dgl/backend/pytorch/tensor.py", line 245, in pad_packed_tensor
    index[cum_lengths[:-1]] += (max_len - lengths[:-1])
IndexError: index 740 is out of bounds for dimension 0 with size 740

Why don't you publish the pre-trained .pt model files for reproducibility?

For device = cpu it fails because it tries to run: self.scale.cuda() in encoder_decoder_layers and transformer_tree_model self.scale is already set to_device this ".cuda()" shouldn't be needed To reproduce try training in a machine with no GPU ---> cd baseline_model && python run_similarity_check.py

Hi, I am trying to compare our results with your results. I am trying to understand the code, could you please explain your training logic for the decompilation code? It's quite confusing. In your cache_tst_ast_1, you have each folder corresponding to one c code (I suppose) and you have multiple files inside them, shouldn't it be one ast per program? Also in the training code, it seems you are randomly selecting some nodes (and also some graphs from cache_test_ast_1?) and generating the remaining AST. Could you please explain the logic behind this? Thank you.

We have some programs with the respective C source code that we can also compile. How do we generate the AST used by N-Bref by clang and tokenize each node in AST? How to generate samples.obj? I didn't find the program to finnish this in this repo.

It is mentioned in the paper that the output of the prediction algorithm is a complete AST. After reading the code we only found a tensor. Is this really the case that there is no part in the code that outputs AST, and if this is the case how you were able to produce source code as in the paper

When I process run_tree_transformer.py I get an error:

Traceback (most recent call last):
  File "run_tree_transformer.py", line 236, in <module>
    main()
  File "run_tree_transformer.py", line 212, in main
    , args.device, criterion, max_len_trg, train_flag=True)
  File "/data/pfps/nbref/baseline_model/data_utils/train_tree_encoder.py", line 246, in train_eval_tree
    model = model.module
  File "/tilde/pfps/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 594, in __getattr__
    type(self).__name__, name))
AttributeError: 'Transformer' object has no attribute 'module'
(nbref) pfps@heaviside:~/nbref/baseline_model$ git status

I think that this is due to config.py having the wrong default for one or two arguments

        parser.add_argument('--parallel_gpu', action='store_true', default=True)
        parser.add_argument('--dist_gpu', action='store_true', default=True)

doesn't make sense because it is impossible to turn these options off. I changed the default to False which allows further progression.

hello. even installing the correct python version 3.7.6 and using pip3.7 install -Iv to install each particular module version, torchtext seems to have difficulties. can you provide an easier onboarding into nbref, an install script or a vm? thanks!