docs/static_site/src/pages/community/code_review.md

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title: Perform Code Reviews
subtitle: General guideline for code reviewers.
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Perform Code Reviews
====================

This is a general guideline for code reviewers. First of all, while it
is great to add new features to a project, we must also be aware that
each line of code we introduce also brings **technical debt** that we
may have to eventually pay.

Open source code is maintained by a community with diverse background,
and hence it is even more important to provide clear, documented and
maintainable code. Code reviews are a shepherding process to spot
potential problems, improve quality of the code. We should, however, not
rely on the code review process to get the code into a ready state.
Contributors are encouraged to polish the code to a ready state before
requesting reviews. This is especially expected for code owner and
committer candidates.

Here are some checklists for code reviews, it is also helpful reference
for contributors.

Hold the Highest Standard
-------------------------

The first rule for code reviewers is to always keep the highest
standard, and do not approve code just to "be friendly". Good,
informative critics each other learn and prevent technical debt in early
stages.

Deliberate on API and Data Structures
-------------------------------------

A minimum and stable API is critical to the project's life. A good API
makes a huge difference. Always think very carefully about all the
aspects including naming, argument definitions and behavior.

When possible, pay more attention still to the proposed API design
during code reviews. Remember, it is easier to improve code
implementation, but it is extremely hard to change an API once accepted.
We should treat data structures that are shared across modules(e.g. AST)
in the same way. If/when uncertain, start a conversation with more
developers before committing.

Here are some useful principles for designing APIs:

-   Be consistent with existing well-known package's APIs if the
    features overlap. For example, tensor operation APIs should always
    be consistent with the numpy API.
-   Be consistent with existing APIs in the same project. For example,
    we should use the same argument ordering across all the optimization
    passes, so there is no "surprise" when using them.
-   Think about whether the API will change in the future. For example,
    we will have more options like loop_unrolling and device placement
    policy as we add more optimizations in build. We can package
    optimization knobs into a build configuration object. In this way,
    the build API is stable over time, even though it may be enriched.
-   Write documentation. Documentation is mandatory for APIs and
    sometimes writing documents helps us to think further about the
    design as well as whether we need to add further clarifications.
-   Minimum. Think about how many lines of code a user has to write to
    use the API. Remove layers of abstraction when possible.

Ensure Test Coverage
--------------------

Each new change of features should introduce test cases. Bug fixes
should include regression tests that prevent the problem from happening
again.

Documentation is Mandatory
--------------------------

Documentation is often overlooked. When adding new functions or changing
an existing function, the documentation should be directly updated. A
new feature is meaningless without documentation to make it accessible.
See more at [Write Document and Tutorials]({% link pages/community/document.md %}).

Minimum Dependency
------------------

Always be cautious in introducing dependencies. While it is important to
reuse code and avoid reinventing the wheel, dependencies can increase
burden of users in deployment. A good design principle is that a feature
or function should only have a dependency if/when a user actually use it.

Ensure Readability
------------------

While it is hard to implement a new feature, it is even harder to make
others understand and maintain the code you wrote. It is common for a
PMC or committer to not be able to understand certain contributions. In
such case, a reviewer should say "I don't understand" and ask the
contributor to clarify. We highly encourage code comments which explain
the code logic along with the code.

Concise Implementation
----------------------

Some basic principles applied here: favor vectorized array code over
loops, use existing APIs that solve the problem.

Document Lessons in Code Reviews
--------------------------------

When you find there are some common or recurring lessons that can be
summarized, add it to the [Code Guide and Tips]({% link pages/community/code_guide.md %}).
It is always good to refer to the guideline document when requesting
changes, so the lessons can be shared to all the community.

Respect each other
------------------

The code reviewers and contributors are paying the most precious
currency in the world \-\- time. We are volunteers in the community to
spend the time to build good code, help each other, learn and have fun
hacking.

Learn from other Code Reviews
-----------------------------

There can be multiple reviewers reviewing the same changes. Many times
other reviewers may spot things you did not find. Try to learn from
other code reviews, when possible, document these lessons.

Approve and Request Changes Explicitly
--------------------------------------

The contributor and code owner can request code reviews from multiple
reviewers. Remember to approve changes when your comments are addressed
in a code review. To do so \-\- please click on changes tab in the pull
request, then select approve, or comment on the code and click request
changes. Code owner can decide if the code can be merged in case by case
if some of the reviewers did not respond in time(e.g. a week) and
existing reviews are sufficient.

Get Started
-----------

Checkout the following [PRs that need review](https://github.com/apache/mxnet/labels/pr-awaiting-review)"

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