CONTRIBUTING.md

Guidelines for contribution

This document described guidelines for contributions for projects led by members of the Poisot lab -- Quantitative and Computational Ecology, Université de Montréal. It should be followed by lab members (at all times), as well as people with suggested added features, or willing to contribute enhancement or bug fixes.

Edits to this document must be suggested as pull requests on the github project located at https://github.com/PoisotLab/PLCG, and not made on the document itself.

It is suggested to include this file to your projects via a Makefile rule:

CONTRIBUTING.md:
  wget -O $@ https://raw.githubusercontent.com/PoisotLab/PLCG/master/README.md

This work is licensed under a Creative Commons Attribution 4.0 International License.

You should have received a copy of the license along with this work. If not, see http://creativecommons.org/licenses/by/4.0/.

General code of behaviour

We follow the Contributor Covenant and (more pragmatically) The No Asshole Rule. No amazing technical or scientific contribution is an excuse for creating a toxic environment.

Issues

Reporting issues is the simplest and most efficient way to contribute. A useful issue includes:

1. The version of all relevant software
2. Your operating system
3. A minimal reproducible example (the shortest code we can copy/paste to reproduce the issue)
4. A description of what was expected to happen
5. A description of what happened instead

Some repositories have issues templates enabled -- if so, they should be used.

Authorship

Any contribution to the code of a software grants authorship on the next paper describing this software, and immediate authorship to the next release (which will receive a DOI). Any contribution to a paper hosted as a public version-controlled repository grants authorship of the paper. For this reason, it is important to correctly identify yourself. For R packages, this is done through the DESCRIPTION file. Whenever a CONTRIBUTORS file is found, add your name to it as part of the workflow described next. For authorship of papers, we go for alphabetical order except for the first and last authors.

Workflow

This section describes the general steps to make sure that your contribution is integrated rapidly. The general workflow is as follows:

1. Fork the repository (see Branches, etc. below)
2. Create an explicitly named branch
3. Create a pull request even if you haven't pushed code yet
4. Be as explicit as possible on your goals
5. Do not squash / rebase commits while you work -- we will do so when merging

Pull requests

Creating a pull request before you push any code will signal that you are interested in contributing to the project. Once this is done, push often, and be explicit about what the commits do (see commits, below). This gives the opportunity for feedback during your work, and allow for tweaks in what you are doing.

A good pull request (in addition to satisfying to all of the criteria below) is:

1. Single purpose - it should do one thing, and one thing only
2. Short - it should ideally involve less than 250 lines of code
3. Limited in scope - it should ideally not span more than a handful of files
4. Well tested and well documented
5. Written in a style similar to the rest of the codebase

This will ensure that your contribution is rapidly reviewed and evaluated.

Branches, etc.

The tagged versions of anything on master are stable releases. The master branch itself is the latest version, which implies that it might be broken at times. For more intensive development, there is usually a dev branch, or feature-specific branches. All significant branches are under continuous integration and code coverage analysis.

Of emojis

Atom's guideline suggest the use of emojis to easily identify what is the purpose of each commit. This is a good idea and should be followed, and it also saves a few characters from the commit first line. Specifically, prepend your commits as follow (adapted from these guidelines).

If the commit is about...	...then use	Example
Work in progress	:construction:	🚧 new graphics
Bug fix	:bug:	🐛 mean fails if NA
Code maintenance	:wrench:	🔧 fix variable names
New test	:rotating_light:	🚨 wget JSON resource
New data	:bar_chart:	📊 example pollination network
New feature	:sparkles:	✨ (anything amazing)
Documentation	:books:	📚 null models wrapper
Performance improvement	:racehorse:	🐎 parallelizes null model by default
Upcoming release	:package:	📦 v1.0.2

Tests and coverage

Most of our repositories undergo continuous integration, and code coverage analysis.

It is expected that:

1. Your commits will not break a passing repo
2. Your commits can make a breaking repo pass
3. Your additions or changes will be adequately tested

This information will be given in the thread of your pull request. Most of our repositories are set-up in a way that new commits that decrease coverage by more than 10% won't be merged.

Good tests make sure that:

1. The code works as it should
2. The code breaks as it should (see Program defensively below)
3. Functions play nicely together

Before merging the content of any pull request, the following tests are done:

1. The branch to be merged from passes the build
2. The future branch (i.e. with the changeset) passes the build
3. The code coverage does not decrease by more than (usually) 10%

Coding practices

Use functions

Don't repeat yourself. If you have to do something more than once, write a function for it. Functions should be as short as possible, and should be single purpose. Think of every piece of code you write as an element of a library.

Program defensively

Check user input, check data structure, and fail often but explicitly.

Bad:

function add(x, y)
  return x + y
end

Acceptable:

function add(x, y)
  @assert typeof(x) == Int64
  @assert typeof(y) == Int64
  return x + y
end

Good:

function add(x, y)
  if typeof(x) != Int64
    throw(TypeError("The first argument should be an Int64", "add", Int64, typeof(x)))
  end
  if typeof(y) != Int64
    throw(TypeError("The second argument should be an Int64", "add", Int64, typeof(y)))
  end
  return x + y
end

(Of course all three solutions are actually bad because they are not the idiomatic way of doing this, but you get the general idea.)

Use type-static functions

Any given function must always return an object of the same type.

A function like y = (x) -> x>3?true:x (returns true if x is larger than 3, else returns x) is not acceptable, as it is hard to predict, hard to debug, and hard to use.

Comment

Comment your code -- comments should not explain what the code does (this is for the documentation), but how it does it.

Variables names

Be as explicit as possible. Have a look at the rest of the codebase before you start. Using i, j, k for loops is, as usual, fine.

Docstrings and documentation

Write some.

R packages must be compiled with roxygen, python code must have docstrings for all documentable objects, and Julia functions must be documented using base docstrings, and jldoctests wherever possible.

There are three levels of documentation: the API documentation (which will be generated from the docstrings), the documentation for fellow developers (which resides ideally entirely in the comments), and documentation for the end users (which include use cases, i.e. how is the feature use in the real world). Your pull request must include relevant changes and additions to the documentation.

Versioning

We use semantic versioning (major.minor.patch). Anything that adds no new feature should increase the patch number, new non-API-breaking changes should increase minor, and major changes should increase major. Any increase of a higher level resets the number after it (e.g, 0.3.1 becomes 1.0.0 for the first major release). It is highly recommended that you do not start working on API-breaking changes without having received a go from us first.