We make changes to Guava's public [APIs][], including adding new APIs, very
carefully. Because of this, if you're interested in seeing a new feature in
Guava, the best approach is to create an [issue][] (or comment on an existing
issue if there is one) requesting the feature and describing specific use cases
for it.

If the feature has merit, it will go through a thorough process of API design
and review. Any code should come after this.

* Create integration tests that exercise a Gradle build for the bug/feature. 
* Annotate tests that correspond to a bug on GitHub (`@Issue("https://github.com/gradle/gradle/issues/2622")`).
* Add documentation to the User Manual and DSL Reference (under [platforms/documentation/docs/src/docs](platforms/documentation/docs/src/docs/)).

 See the License for the specific language governing permissions and
 limitations under the License.
-->
Contributing to Apache Maven
======================

You have found a bug or you have an idea for a cool new feature? Contributing
code is a great way to give something back to the open source community. Before
you dig right into the code, there are a few guidelines that we need

$ cd minio
$ git remote add upstream https://github.com/minio/minio
$ git fetch upstream
$ git merge upstream/master
...
```

### Create your feature branch

Before making code changes, make sure you create a separate branch for these changes

```
git checkout -b my-new-feature
```

### Test MinIO server changes

After your code changes, make sure

If you've found a security problem, please follow our [bug bounty][security] program.

If you've found a bug, please contribute a failing test case so we can study and fix it.

If you have a new feature idea, please build it in an external library. There are
[many libraries][works_with_okhttp] that sit on top or hook in via existing APIs. If you build
something that integrates with OkHttp, tell us so that we can link it!

*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in