Programming Language Identification

Introduction

Motivated by the amount of code that goes unidentified on the web, I have explored the existing practical methods for algorithmically identifying the programming language of source code.

Literature

Programming Language Identification (PLI)—also referred to as Source Code Classification (SCC) in the literature—is the problem of identifying the programming language in which a given source code file, or just a short code snippet, is written in. PLI is a common preliminary need in automated program comprehension and also a relevant practical problem for both practitioners and researchers, with important applications in programming trend analysis (Chen et al., 2005), mining software repositories (Mockus, 2009; Caneill, Germn & Zacchiroli, 2017), source code indexing, and code search (Gallardo-Valencia & Sim, 2009; Kononenko et al., 2014).

(Del Bonifro et al., 2021)

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

PREREQUISITES

Download the Rosetta submodule:

$ git submodule update --init --recursive

you can find all the references for the Rosetta project at this link.

Apple M1

If you are using the new Apple M1 chip please be sure to have installed hdf5 by running:

$ brew install hdf5

INSTALLING

Create and enable the virtual environment:

$ mkdir venv
$ python -m venv venv
$ source venv/bin/activate

below you can find all the scripts for installing based on your OS/processor

$ make
  >  +------------------------------------------------------+
  >  |         OS         |  Hardware  |    Setup Command   |
  >  +------------------------------------------------------+
  >  |   Windows/Linux    |   - GPU    |  'make setup.CPU'  |
  >  |    Apple macOS     |    + M1    |  'make setup.M1'   |
  >  |    Apple macOS     |    - M1    |  'make setup.CPU'  |
  >  +------------------------------------------------------+

for instance if you have MacOS with Intel chip you have to run:

$ make setup.CPU

Running the tests

You can train/test the program by running:

$ make run

or alternatively

$ python programming_languages_classification/main.py

Built With

• Tensorflow - Open Source ML Framework
• Rosetta - RosettaCode Dataset

Authors

• Matteo Conti - author - contimatteo

License

This project is licensed under the MIT License - see the LICENSE.md file for details