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Code checking tool for teaching Python

Project description

PyTA

PyTA is a Python program which uses static code analysis to help students find and fix common coding errors in introductory Python courses. Python already has great static analysis tools like pep8 and pylint, but these tools do not necessarily have the most beginner-friendly format. PyTA has two central goals:

  1. Statically identify common coding errors by using existing linting tools and building custom linters (e.g., as pylint checkers).
  2. Present beautiful, intuitive messages to students that are both helpful for fixing errors, and good preparation for the terser messages they will see in their careers.

This is a new project started in the Summer of 2016, and takes the form of a wrapper around pylint (with custom checkers) that operates directly on Python modules, as well as a website with some supplementary material going into further detail for the emitted errors.

For greater details on the errors PyTA checks for: Help Documentation.

For help getting started using PyTA: Quick Start.

Installation

If you're interested in using PyTA, you can install it using pip (or pip3, on OSX/Linux):

> pip install python-ta

If you're developing PyTA, first clone this repo, and then run pip install -e .[dev] from inside your local copy of the repo. Note that some debugging tools require graphviz to be installed on your system.

Tests

To run the test suite, run the following command from inside the pyta directory:

> python -m pytest tests  # Or python3 on OSX/Linux

Demo

You can currently see a proof of concept in this repository. Clone it, and then run python in this directory (PyTA is primarily meant to be included as a library). In the Python interpreter, try running:

>>> import python_ta
>>> python_ta.check_all('examples.forbidden_import_example')
[Some output should be shown]
>>> python_ta.doc('E9999')

Contributors

Nigel Fong, Adam Gleizer, Ibrahim Hasan, Niayesh Ilkhani, Rebecca Kay, Christopher Koehler, David Kim, Simeon Krastnikov, Ryan Lee, Hayley Lin, Wendy Liu, Shweta Mogalapalli, Ignas Panero Armoska, Justin Park, Amr Sharaf, Kavin Singh, Alexey Strokach, Jasmine Wu, Philippe Yu

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