These guidelines instruct how to submit issues and contribute code or documentation to the Robot Framework project. There are also many other projects in the larger Robot Framework ecosystem that you can contribute to. If you notice a library or tool missing, there is hardly any better way to contribute than creating your own project. Other great ways to contribute include answering questions and participating discussion on robotframework-users mailing list and other forums, as well as spreading the word about the framework one way or the other.
These guidelines expect readers to have a basic knowledge about open source as well as why and how to contribute to open source projects. If you are totally new to these topics, it may be a good idea to look at the generic Open Source Guides first.
Bugs and enhancements are tracked in the issue tracker. If you are unsure if something is a bug or is a feature worth implementing, you can first ask on robotframework-users mailing list, on IRC (#robotframework on irc.freenode.net), or on Slack. These and other similar forums, not the issue tracker, are also places where to ask general questions.
Before submitting a new issue, it is always a good idea to check is the same bug or enhancement already reported. If it is, please add your comments to the existing issue instead of creating a new one.
Explain the bug you have encountered so that others can understand it and preferably also reproduce it. Key things to have in good bug report:
- Version information
- Robot Framework version
- Python interpreter type (Python, Jython, IronPython, PyPy) and version
- Operating system and its version
- Steps to reproduce the problem. With more complex problems it is often a good idea to create a short, self contained, correct example (SSCCE).
- Possible error message and traceback.
Notice that all information in the issue tracker is public. Do not include any confidential information there.
Describe the new feature and use cases for it in as much detail as possible. Especially with larger enhancements, be prepared to contribute the code in the form of a pull request as explained below or to pay someone for the work. Consider also would it be better to implement this functionality as a separate tool outside the core framework.
If you have fixed a bug or implemented an enhancement, you can contribute your changes via GitHub's pull requests. This is not restricted to code, on the contrary, fixes and enhancements to documentation and tests alone are also very valuable.
Often you already have a bug or an enhancement you want to work on in your mind, but you can also look at the issue tracker to find bugs and enhancements submitted by others. The issues vary significantly in complexity and difficulty, so you can try to find something that matches your skill level and knowledge. There are two specific labels to look for when looking for something to contribute:
- good first issue
- These issues typically do not require any knowledge of Robot Framework internals and are generally easy to implement or fix. Thus these issues are especially good for new contributors.
- help wanted
- These issues require external help to get implemented or fixed.
On GitHub pull requests are the main mechanism to contribute code. They are easy to use both for the contributor and for the person accepting the contribution, and with more complex contributions it is easy also for others to join the discussion. Preconditions for creating pull requests are having a GitHub account, installing Git and forking the Robot Framework project.
GitHub has good articles explaining how to set up Git, fork a repository and use pull requests and we do not go through them in more detail. We do, however, recommend to create dedicated topic branches for pull requests instead of creating them based on the master branch. This is especially important if you plan to work on multiple pull requests at the same time.
Robot Framework uses the general Python code conventions defined in PEP-8. In addition to that, we try to write idiomatic Python and follow the SOLID principles with all new code. An important guideline is that the code should be clear enough that comments are generally not needed.
All code, including test code, must be compatible with all supported Python interpreters and versions. Most importantly this means that the code must support both Python 2 and Python 3.
Maximum line length with Python code, including docstrings and comments, is 88 characters. This is also what Black uses by default and their documentation explains why. Notice that we do not have immediate plans to actually take Black into use but we may consider that later.
With Robot Framework tests the maximum line length is 100.
We are pretty picky about using whitespace. We follow PEP-8 in how to use blank lines and whitespace in general, but we also have some stricter rules:
- No blank lines inside functions.
- No blank lines between a class declaration and class attributes or between attributes.
- Indentation using spaces, not tabs.
- No trailing spaces.
- No extra empty lines at the end of the file.
- Files must end with a newline.
Most of these rules are such that any decent text editor or IDE can be configured to automatically format files according to them.
Docstrings should be added to public APIs, but they are not generally needed in internal code. When docstrings are added, they should follow PEP-257. See API documentation section below for more details about documentation syntax, generating API docs, etc.
With new features adequate documentation is as important as the actual functionality. Different documentation is needed depending on the issue.
Robot Framework's features are explained in the User Guide. It is generated using a custom script based on the source in reStructuredText format. For more details about editing and generating it see doc/userguide/README.rst.
If standard libraries distributed with Robot Framework are enhanced, also their documentation needs to be updated. Keyword documentation is created from docstrings using the Libdoc tool. Documentation must use Robot Framework's own documentation formatting and follow these guidelines:
- Other keywords and sections in the library introduction can be referenced
with internal links created with backticks like
`Example Keyword`. - When referring to arguments, argument names must use inline code style
created with double backticks like
``argument``. - Examples are recommended whenever the new keyword or enhanced functionality is not trivial.
- All new enhancements or changes should have a note telling when the change
was introduced. Often adding something like
New in Robot Framework 3.1.is enough.
Library documentation can be generated using Invoke by running command
invoke library-docs <name>
where <name> is the name of the library or its unique prefix. Run
invoke --help library-docs
for more information see BUILD.rst for details about installing and using Invoke.
Modules and classes defined to be public should have API documentation. We do not generally use API docs with internal code because it is so hard to keep the docs in sync with the code. Instead we try to keep the code as clean and easy to understand as possible.
API docs are created using docstrings following guidelines defined in PEP-257. They are converted to HTML using Sphinx and its autodoc extension. Documentation can be created locally using doc/api/generate.py script that unfortunately creates a lot of errors on the console. Releases API docs are visible at https://robot-framework.readthedocs.org/.
Robot Framework's public API docs are lacking in many ways. All public classes are not yet documented, existing documentation is somewhat scarce, and there could be more examples. Documentation improvements are highly appreciated!
When submitting a pull request with a new feature or a fix, you should always include tests for your changes. These tests prove that your changes work, help prevent bugs in the future, and help document what your changes do. Depending on the change, you may need acceptance tests, unit tests or both.
Make sure to run all of the tests before submitting a pull request to be sure that your changes do not break anything. If you can, test in multiple environments and interpreters (Windows, Linux, OS X, Python, Jython, IronPython, Python 3, etc). Pull requests are also automatically tested on continuous integration.
If you want to manually verify the changes, an easy approach is directly
running the src/robot/run.py script that is part of Robot Framework
itself. Alternatively you can use the rundevel.py script that sets
some command line options and environment variables to ease executing tests
under the atest/testdata directory. It also automatically creates a
tmp directory in the project root and writes all outputs there.
If you want to install the current code locally, you can do it like
python setup.py install as explained in INSTALL.rst. For
instructions how to create a distribution that allows installing elsewhere
see BUILD.rst.
Most of Robot Framework's testing is done using acceptance tests that naturally use Robot Framework itself for testing. Every new functionality or fix should generally get one or more acceptance tests. See atest/README.rst for more details about creating and executing them.
Unit tests are great for testing internal logic and should be added when appropriate. For more details see utest/README.rst.