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-# Neural Variable Renaming
-
-This repository contains the neural variable renaming model `DIRE` from our ASE 2019 paper *DIRE: A Neural Approach to Decompiled Identifier Renaming*.
-
-## Conda Environment
-
-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 create -f data/env.yml
-conda activate var_rename
-```
-
-## Dataset and Preprocessing
-
-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](https://doi.org/10.5281/zenodo.3403077).
-
-### Pre-process the Github Binaries Dataset for DIRE
-
-**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:
-
-```bash
-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`](http://docopt.org/), 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](https://drive.google.com/drive/folders/19Rf7NtW56r6fz-ycldZq9hjxNr5osAJW?usp=sharing). 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:
-
-```bash
-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:
-
-```bash
-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. 
-
-## Running DIRE
-
-`exp.py` is the entry script for training and evaluating the DIRE model. Below is an example training script:
-
-```bash
-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. 
- 
- ```bash
-python exp.py \
-    test \
-    --cuda \
-    --extra-config='{"data": {"vocab_file": "data/vocab.bpe10000/vocab"}, "decoder": {"remove_duplicates_in_prediction": true} }' \
-    data/saved_models/model.hybrid.bin \   # path to the pretrained models at `data/saved_models` or the saved model under the user-specified work directory
-    data/preprocessed_data/test.tar
-```
-
-### Pretrained Models
-
-We also provide pre-trained DIRE models used in our paper, located under `data/saved_models/`.
-
-## Reference
-
-```
-@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}
-}
-```