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DIRTY: Augmenting Decompiler Output with Learned Variable Names and Types

Code | Paper PDF | Demo

Original implementation for paper Augmenting Decompiler Output with Learned Variable Names and Types.

DIRTY is a Transformer-based model which improves the quality of decompiler outputs by automatically generating meaningful variable names and types, assigning variable names that agree with those written by developers 66.4% of the time and types 75.8% of the time. We also release a large real-world dataset DIRT for this task, which consists of 75K+ programs and 1M+ human-written C functions mined from GitHub paired with their decompiler outputs.

Installation

Requirements

Quick Start

Training

Download DIRT

The first step to train DIRTY is to download the preprocessed DIRT dataset. If you would like the full, unpreprocessed dataset, it is available at https://doi.org/10.1184/R1/20732656.v1

cd dirty/
wget cmu-itl.s3.amazonaws.com/dirty/dirt.tar.gz -O dirt.tar.gz
tar -xzf dirt.tar.gz

The command would automatically download and decompress the dataset from Amazon S3. If your machine does not have access to AWS, please manually download from the above link and untar it to data1/.

Our dataset was generated using GHCC, which was fed the list of projects found in the projects.txt file. This list was generated by querying the GHTorrent database in late 2019, and contains projects from January 28, 2008 through May 31, 2019. Note that not all of these projects compiled, and it is likely that some of them no longer exist.

Train DIRTY

We have setup configuration files for different models reported in our paper:

file model time (estimated hours)
multitask.xfmr.jsonnet DIRTY-Multitask 120
rename.xfmr.jsonnet DIRTY-Rename 80
retype.xfmr.jsonnet DIRTY-Retype 80
retype_nomem.xfmr.jsonnet DIRTY_NDL 80
retype_base.xfmr.jsonnet DIRTY_S 40

Training a models is as easy as specifying the name of the experimental run and the config file. Suppose we want to reproduce the Multi-task model in Table~7 in the paper:

cd dirty/
python exp.py train --cuda --expname=dirty_mt multitask.xfmr.jsonnet

Then, please watch for the line wandb: Run data is saved locally in ... in the output. This is where the logs and models are to be saved. You can also monitor the automatically uploaded training and validation status (e.g., losses, accuracy) in your browser in real-time with the link printed after wandb: 🚀 View run at ....

Feel free to adjust the hyperparameters in *.jsonnet config files to train your own model.

Inference

Download Trained Model

As an alternative to train the model by yourself, you can download our trained DIRTY model.

cd dirty/
mkdir exp_runs/
wget cmu-itl.s3.amazonaws.com/dirty/dirty_mt.ckpt -O exp_runs/dirty_mt.ckpt

Test DIRTY

First, run your trained/downloaded model to produce predictions on the DIRE test set.

python exp.py train --cuda --expname=eval_dirty_mt multitask.xfmr.jsonnet --eval-ckpt <ckpt_path>

<ckpt_path> is either exp_runs/dirty_mt.ckpt if you download our trained model, or saved during training at wandb/run-YYYYMMDD_HHMMSS-XXXXXXXX/files/dire/XXXXXXXX/checkpoints/epoch=N.ckpt.

We sugguest changing beam_size in config files to 0 to switch to greedy decoding, which is significantly faster. The default configuration of beam_size = 5 can take hours.

The predictions will be saved to pred_XXX.json. This filename depends on models and can be modified in config files. You can inspect the prediction results, which is in the following format.

{
  binary: {
    func_name: {
      var1: [var1_retype, var1_rename], ...
    }, ...
  }, ...
}

Finally, use our standalone benchmark script:

python -m utils.evaluate --pred-file pred_mt.json --config-file multitask.xfmr.jsonnet

Structure

Here is a walk-through of the code files of this repo.

dirty/

The dirty/ folder contains the main code for the DIRTY model and DIRT dataset.

dirty/exp.py

The entry point for running DIRTY experiments. It loads a configuration file, constructs a dataset instance, a model instance, and launches into a Trainer which runs training or inference according to configuration, and save logs and results in wandb.

dirty/*.xfmr.jsonnet

Configuration files for running DIRTY experiments.

dirty/model

This folder contains neural models consisting of the DIRTY model.

├── dirty
│   ├── model
│   │   ├── beam.py                     # Beam search
│   │   ├── decoder.py                  # factory class for building Decoders from configs
│   │   ├── encoder.py                  # factory class for building Encoders from configs
│   │   ├── model.py                    # training and evaluation step and metric logging
│   │   ├── simple_decoder.py           # A `decoder' consists of a linear layer,
                                        # used for producing a soft mask from Data Layout Encoder
│   │   ├── xfmr_decoder.py             # Type/Multitask Decoder
│   │   ├── xfmr_mem_encoder.py         # Data Layout Encoder
│   │   ├── xfmr_sequential_encoder.py  # Code Encoder
│   │   └── xfmr_subtype_decoder.py     # Not used in the current version

dirty/utils

This folder contains code for the DIRT dataset, data preprocessing, evaluation, helper functions, and demos in the paper.

├── dirty
│   └── utils
│       ├── case_study.py           # Generate results for Table 3 and Table 6 in the paper
│       ├── code_processing.py      # Code canonicalization such as converting literals
│       ├── compute_mi.py           # Compute the mutual information between variables and types as a proof-of-concept for MT
│       ├── dataset.py              # A parallelized data loading class for preparing batched samples from DIRT for DIRTY
│       ├── dataset_statistics.py   # Compute dataset statistics
│       ├── dire_types.py -> ../../binary/dire_types.py
│       ├── evaluate.py             # Evaluate final scores from json files saved from different methods for fair comparison
│       ├── function.py -> ../../binary/function.py
│       ├── ida_ast.py -> ../../binary/ida_ast.py
│       ├── lexer.py
│       ├── preprocess.py           # Preprocess data produced from `dataset-gen/` into the DIRT dataset
│       ├── util.py
│       ├── variable.py -> ../../binary/variable.py
│       └── vocab.py

dirty/baselines

Empirical baselines included in the paper. Use python -m baselines.<xxxxxx> to run. Results are saved to corresponding json files and can be evaluated with python -m utils.evaluate.

├── dirty
│   ├── baselines
│   │   ├── copy_decompiler.py
│   │   ├── most_common.py
│   │   └── most_common_decomp.py

binary/

The binary/ folder contains definitions for classes, including types, variables, and functions, constructed from decompiler outputs from binaries.

├── binary
│   ├── __init__.py     
│   ├── dire_types.py   # constructing types and a type library
│   ├── function.py     # definition and serialization for function instances
│   ├── ida_ast.py      # constructing ASTs from IDA-Pro outputs
│   └── variable.py     # definition and serialization for variable instances

idastubs/

The idastubs/ folder contains helper functions used by the ida_ast.py file.

dataset-gen/

The dataset-gen/ folder contains producing unpreprocessed data from binaries using IDA-Pro (required).

dire/

Legacy code for the DIRE paper.

Citing DIRTY

If you use DIRTY/DIRT in your research or wish to refer to the baseline results, please use the following BibTeX.

@inproceedings {chen2021augmenting,
  title = {Augmenting Decompiler Output with Learned Variable Names and Types},
  author = {Chen, Qibin and Lacomis, Jeremy and Schwartz, Edward J. and {Le~Goues}, Claire and Neubig, Graham and Vasilescu, Bogdan},
  booktitle = {31st USENIX Security Symposium},
  year = {2022},
  address = {Boston, MA},
  url = {https://www.usenix.org/conference/usenixsecurity22/presentation/chen-qibin},
  month = aug,
}

@inproceedings {lacomis2019dire,
  title = {DIRE: A Neural Approach to Decompiled Identifier Naming},
  author = {Lacomis, Jeremy and Yin, Pengcheng and Schwartz, Edward J. and Allamanis, Miltiadis and {Le~Goues}, Claire and Neubig, Graham and Vasilescu, Bogdan},
  booktitle = {34th IEEE/ACM International Conference on Automated Software Engineering},
  year = {2019},
  address = {San Diego, CA},
  pages = {628--639},
}