Source Code Language Model (SCLM)

We created a language model for source code in order to find buggy code. The intuition is that snippest of code with high Entropy according to the language model are likely to be buggy. See the project slides for more information.

Authors:
Jack Lanchantin (http://cs.virginia.edu/~jjl5sw/)
Ji Gao (http://www.cs.virginia.edu/~jg6yd/)

This code was modified from https://github.com/jcjohnson/torch-rnn

Installation

System setup

You'll need to install the header files for Python 2.7 and the HDF5 library. On Ubuntu you should be able to install like this:

sudo apt-get -y install python2.7-dev
sudo apt-get install libhdf5-dev

Python setup

The preprocessing script is written in Python 2.7; its dependencies are in the file requirements.txt. You can install these dependencies in a virtual environment like this:

virtualenv .env                  # Create the virtual environment
source .env/bin/activate         # Activate the virtual environment
pip install -r requirements.txt  # Install Python dependencies
# Work for a while ...
deactivate                       # Exit the virtual environment

Lua setup

The main modeling code is written in Lua using torch; you can find installation instructions here. You'll need the following Lua packages:

• torch/torch7
• torch/nn
• torch/optim
• lua-cjson
• torch-hdf5

After installing torch, you can install / update these packages by running the following:

# Install most things using luarocks
luarocks install torch
luarocks install nn
luarocks install optim
luarocks install lua-cjson

# We need to install torch-hdf5 from GitHub
git clone https://github.com/deepmind/torch-hdf5
cd torch-hdf5
luarocks make hdf5-0-0.rockspec

CUDA support (Optional)

To enable GPU acceleration with CUDA, you'll need to install CUDA 6.5 or higher and the following Lua packages:

• torch/cutorch
• torch/cunn

You can install / update them by running:

luarocks install cutorch
luarocks install cunn

Usage

To train a model and use it to generate new text, you'll need to follow three simple steps:

Step 1: Preprocess the data

You can use any text file for training models. Before training, you'll need to preprocess the data using the script scripts/preprocess.py; this will generate an HDF5 file and JSON file containing a preprocessed version of the data.

If you have training data stored in my_data.txt, you can run the script like this:

python scripts/preprocess.py \
  --input_txt my_data.txt \
  --output_h5 my_data.h5 \
  --output_json my_data.json

This will produce files my_data.h5 and my_data.json that will be passed to the training script.

There are a few more flags you can use to configure preprocessing; read about them here

Step 2: Train the model

After preprocessing the data, you'll need to train the model using the train.lua script. This will be the slowest step. You can run the training script like this:

th train.lua -input_h5 my_data.h5 -input_json my_data.json

This will read the data stored in my_data.h5 and my_data.json, run for a while, and save checkpoints to files with names like cv/checkpoint_1000.t7.

You can change the RNN model type, hidden state size, and number of RNN layers like this:

th train.lua -input_h5 my_data.h5 -input_json my_data.json -model_type rnn -num_layers 3 -rnn_size 256

By default this will run in GPU mode using CUDA; to run in CPU-only mode, add the flag -gpu -1.

To run with OpenCL, add the flag -gpu_backend opencl.

There are many more flags you can use to configure training; read about them here.

Step 3: Sample from the model

After training a model, you can generate new text by sampling from it using the script sample.lua. Run it like this:

th sample.lua -checkpoint cv/checkpoint_10000.t7 -length 2000

This will load the trained checkpoint cv/checkpoint_10000.t7 from the previous step, sample 2000 characters from it, and print the results to the console.

By default the sampling script will run in GPU mode using CUDA; to run in CPU-only mode add the flag -gpu -1 and to run in OpenCL mode add the flag -gpu_backend opencl.

There are more flags you can use to configure sampling; read about them here.