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Function Parser

This repository contains various utils to parse GitHub repositories into function definition and docstring pairs. It is based on tree-sitter to parse code into ASTs and apply heuristics to parse metadata in more details. Currently, it supports 6 languages: Python, Java, Go, Php, Ruby, and Javascript.

It also parses function calls and links them with their definitions for Python.

Examples

Input library keras-team/keras is parsed into list of functions including various metadata (e.g. identifier, docstring, sha, url, etc.). Below is an example output of Activation function from keras library.

{
    'nwo': 'keras-team/keras',
    'sha': '0fc33feb5f4efe3bb823c57a8390f52932a966ab',
    'path': 'keras/layers/core.py',
    'language': 'python',
    'identifier': 'Activation.__init__',
    'parameters': '(self, activation, **kwargs)',
    'argument_list': '',
    'return_statement': '',
    'docstring': '',
    'function': 'def __init__(self, activation, **kwargs):\n        super(Activation, self).__init__(**kwargs)\n        self.supports_masking = True\n        self.activation = activations.get(activation)',
    'url': 'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'
}

One example of Activation in the call sites of eriklindernoren/Keras-GAN repository is shown below:

{
    'nwo': 'eriklindernoren/Keras-GAN',
    'sha': '44d3320e84ca00071de8a5c0fb4566d10486bb1d',
    'path': 'dcgan/dcgan.py',
    'language': 'python',
    'identifier': 'Activation',
    'argument_list': '("relu")',
    'url': 'https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dcgan/dcgan.py#L61-L61'
}

With an edge linking the two urls

(
    'https://github.com/eriklindernoren/Keras-GAN/blob/44d3320e84ca00071de8a5c0fb4566d10486bb1d/dcgan/dcgan.py#L61-L61',
    'https://github.com/keras-team/keras/blob/0fc33feb5f4efe3bb823c57a8390f52932a966ab/keras/layers/core.py#L294-L297'
)

A demo notebook is also provided for exploration.

Usages

To run the notebook on your own:

  1. script/bootstrap to build docker container
  2. script/server to run the jupyter notebook server and navigate to function_parser/demo.ipynb

To run the script:

  1. script/bootstrap to build docker container
  2. script/setup to download libraries.io data
  3. script/console to ssh into the container
  4. Inside the container, run python function_parser/process.py --language python --processes 16 '/src/function-parser/data/libraries-1.4.0-2018-12-22/' '/src/function-parser/data/'