This Repo is deprecated, please refer to https://github.com/CMUSTRUDEL/DIRE for latest updates

This repository contains the neural variable renaming model DIRE from our ASE 2019 paper DIRE: A Neural Approach to Decompiled Identifier Renaming.

First, download all supporting files:

wget http://www.cs.cmu.edu/~pengchey/dire_models.zip
unzip dire_models.zip

To install and activate the conda environment:

conda env install -f data/env.yml
conda activate var_rename

We created a corpus of 164,632 unique x86-64 binaries generated from C projects mined from GitHub. Each binary is decompiled by hexray. To download the full dataset, please visit here.

Clearning Binary Data To train and test DIRE model using the collected binaries dataset, first run the following pre-process script utils.preprocess to (1) filter invalid examples (e.g., code with too-large ASTs), and (2) randomly partition the entire dataset into training/development/test sets:

mkdir -p data/preprocessed_data

python -m utils.preprocess \
    "path/to/binary/dataset/*.tar.gz" \   # use wild-card to match all tar files
    data/preprocessed_data

All scripts are documented using docopt, please refer to the docstring of utils/preprocess.py for its complete usage.

Our Preprocessed Splits You may also download our pre-processed dataset along with the training/testing splits from here. The pre-processing scripts also support fixing the testing set to be a pre-defined partition. For example, to use the same testing partition as the one used in our paper during pre-processing, you may run:

python -m utils.preprocess \
    --no-filtering \                                # optional: do not perform filtering 
    --test-file=path/to/predefined/test_file.tar \
    "path/to/binary/dataset/*.tar.gz" \
    data/preprocessed_data

Vocabulary Files We've included the vocabulary file in the release (under data/vocab.bpe10000). If you would like to create your own vocabulary (e.g., to try a different BPE vocabulary size), simply run:

python -m utils.vocab \
    --use-bpe \
    --size=10000 \
    "data/preprocessed_data/train-shard-*.tar" \
    data/vocab.bpe10000

Again, please refer to the script file's docstring for its complete usage.

exp.py is the entry script for training and evaluating the DIRE model. Below is an example training script:

mkdir -p exp_runs/dire.hybrid   # create a work directory

python exp.py \
    train \
    --cuda \
    --work-dir=exp_runs/dire.hybrid \
    --extra-config='{ "data": {"train_file": "data/preprocessed_data/train-shard-*.tar" }, "decoder": { "input_feed": false, "tie_embedding": true }, "train": { "evaluate_every_nepoch": 5, "max_epoch": 60 } }' \
    data/config/model.hybrid.jsonnet

DIRE uses json.net for programmable configuration. Extra configs could be specified using the --extra-config argument.

To evaluate a saved or pretrained model, run the following command.

python exp.py \
   test \
   --cuda \
   --extra-config='{"decoder": {"remove_duplicates_in_prediction": true} }' \
   exp_runs/dire.hybrid/model.iter_number.bin \   # path to the saved model under the work directory
   data/preprocessed_data/test.tar

We also provide pre-trained DIRE models used in our paper, located under data/saved_models/.

@inproceedings{lacomis19ase,
    title = {{DIRE}: A Neural Approach to Decompiled Identifier Renaming},
    author = {Jeremy Lacomis and Pengcheng Yin and Edward J. Schwartz and Miltiadis Allamanis and Claire Le Goues and Graham Neubig and Bogdan Vasilescu},
    booktitle = {34th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
    address = {San Diego, California},
    month = {November},
    year = {2019}
}