Skip to content

Latest commit

 

History

History
161 lines (99 loc) · 5.31 KB

CONTRIBUTING.rst

File metadata and controls

161 lines (99 loc) · 5.31 KB
Raw

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at bugzilla.

If you are reporting a bug, please include:

  • Your operating system name and version.
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

Write Documentation

Glean Parser could always use more documentation, whether as part of the official Glean Parser docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at TODO

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here's how to set up glean_parser for local development.

  1. Fork the glean_parser repo on GitHub.

  2. Clone your fork locally:

    $ git clone git@github.com:your_name_here/glean_parser.git

  3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

    $ mkvirtualenv glean_parser
$ cd glean_parser/
$ python setup.py develop

  4. Create a branch for local development:

    $ git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

  5. To test your changes to glean_parser:

    Install the testing dependencies:

    $ pip install -r requirements_dev.txt

    If using Python 3.5:

    $ pip install -r requirements_dev_35.txt

    Optionally, if you want to ensure that the generated Kotlin code lints correctly, install a Java SDK, and then run:

    $ make install-kotlin-linters

    Then make sure that all lints and tests are passing:

    $ make lint
$ make test

  6. Commit your changes and push your branch to GitHub:

    $ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

  7. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include tests.
  2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
  3. The pull request should work for Python 3.5, 3.6, 3.7 and 3.8 (The CI system will take care of testing all of these Python versions).
  4. The pull request should update the changelog in HISTORY.rst.

Tips

To run a subset of tests:

$ py.test tests.test_glean_parser

Deploying

A reminder for the maintainers on how to deploy.

Get a clean master branch with all of the changes from upstream:

$ git checkout master
$ git fetch upstream
$ git rebase upstream/master

  • Update the header with the new version and date in HISTORY.rst.

  • (By using the setuptools-scm package, there is no need to update the version anywhere else).

  • Make sure all your changes are committed.

  • Push the changes upstream:

    $ git push upstream master

  • Wait for [continuous integration to pass](https://circleci.com/gh/mozilla/glean/tree/master) on master.

  • Make the release on GitHub using [this link](https://github.com/mozilla/glean_parser/releases/new)

  • Enter the new version in the form vX.Y.Z.

  • Copy and paste the relevant part of the HISTORY.rst file into the description.

The continuous integration system will then automatically deploy to PyPI.