/codenn

Summarizing Source Code using a Neural Attention Model - CODENN

Primary LanguagePythonMIT LicenseMIT

Forked from sriniiyer/codenn

Extra notes on installation:

  • Torch installation:
    • to overcome the LuaJIT low memory limit when using torch.save, install torch with Lua 5.2 instead of LuaJIT
      use ./clean.sh; TORCH_NVCC_FLAGS="-D__CUDA_NO_HALF_OPERATORS__" CC=/usr/bin/gcc-6 CXX=/usr/bin/g++-6 TORCH_LUA_VERSION=LUA52 ./install.sh to install Torch.
    • (build-essential, gcc, g++, libreadline-dev, cmake)
  • install cuda
  • Cutorch installation: torch/cutorch
    luarocks install cutorch, luarocks install cunn
  • (for data preprocessing) antlr4: we need version 4.5 specified in src/csharp/CSharp4Lexer.py
    python2 -m pip install 'antlr4-python2-runtime>=4.5,<4.6'
  • (for data preprocessing) antlr4-complete: to create a parser for Java, we need to download:
    curl -O http://www.antlr.org/download/antlr-4.5.3-complete.jar
    export CLASSPATH=".:[path: antlr-4.5.3-complete.jar]:$CLASSPATH"

Original README

Run CODENN

See details of CODENN in our paper

Summarizing Source Code using a Neural Attention Model (https://github.com/sriniiyer/codenn/blob/master/summarizing_source_code.pdf)

Requirements

Setup environment

export PYTHONPATH=~/codenn/src/:~/codenn/src/sqlparse export CODENN_DIR=~/codenn/ export CODENN_WORK=./workdir

Build both csharp and sql datasets

Install modified sqlparse

cd src/sqlparse/ sudo python setup.py install

Build datasets

cd src/model ./buildData.sh

Train codenn models and predict on test set

./run.sh {sql|csharp}