If you always search for ways to increase code quality and would like to encourage everyone in your project to keep high test code coverage all the time, then Codecov may be another step in your journey.

You can think of it as an automated code reviewer which constantly reminds you what you can do to improve your test code coverage. It can be set up to perform build-failing checks on your code changes if they would degrade code coverage or even suggest which files need to be covered by tests.

This article shows how to set up an open source Android project integration with Codecov as a part of a Continuous Integration process with GitHub as a repository host and Travis CI as a build server.

Generating code coverage reports

In order to get code coverage reports you have to gather test execution data first. This process may differ between a Java module, an Android library project or an actual Android application project. Following paragraphs describe the necessary configuration based on Gradle build system.

Unit tests

To generate unit tests’ code coverage reports for an Android application or library project some additional work may be needed. Currently, Android Plugin for Gradle does not generate Gradle report tasks out of the box. One solution is to configure them manually or to use jacoco-android-gradle-plugin that configures code coverage report tasks automatically:

buildscript {
  dependencies {
    classpath 'com.dicedmelon.gradle:jacoco-android:0.1.1'

apply plugin: 'jacoco-android'

To generate code coverage reports for all variants of an application or library you just run jacocoTestReport Gradle task:

$ gradle jacocoTestReport

Now, you can browse to build/reports/jacoco/<variant_name> directory and see some nicely generated HTML reports. By default, the plugin also provides an XML output which will be used by Codecov later on.

The plugin also contains some predefined exclusion filters for generated classes so they do not end up in code coverage reports. If there is still a need to provide some additional exclusion filters it is possible to add them in the plugin extension block:

jacocoAndroidUnitTestReport {
  jacocoExcludes += [ '**/AutoValue*.class' ]

Instrumentation tests

Apart from testing on a local JVM, Android supports running tests on a real device or an emulator using Instrumentation. In case of instrumentation tests it is a little bit less hassle. You do not need to create code coverage report tasks manually. They are already created by the Android Gradle plugin. All you have to do is set testCoverageEnabled to true in your build type configuration, e.g.:

android {
  buildTypes {
    debug {
      testCoverageEnabled true

Running UI tests and generating their code coverage reports can be done by running connectedCheck Gradle task.

There is just a slight problem if you want to gather code coverage execution data on a real device as there are some devices which do not allow that out of the box. Still, if you are using an emulated environment it works rather well.

Unit tests in pure Java project

Fortunately, in case of a pure Java project close to no configuration is required in order to get code coverage reports. You just apply the jacoco plugin with XML output enabled:

apply plugin: 'jacoco'

jacocoTestReport {
  reports {
    xml.enabled true

and you are ready to launch test and jacocoTestReport tasks.

Pushing reports to Codecov as a CI process step

The real power of integrating code coverage reports with Codecov comes from setting it as a part of the CI process. This section describes such integration with an open source project hosted at GitHub and built by Travis CI.

Whenever developers make a pull request to the project it would be great for them to see reports on how merging their changes may affect the code coverage.

To achieve this with Travis CI a proper .travis.yml located in the project root sets the environment to build an Android project.

language: android
jdk: oraclejdk8
    - ANDROID_TARGET=android-15
    - ANDROID_ABI=armeabi-v7a
  - tools
  - platform-tools
  - build-tools-23.0.2
  - android-23
  - extra-android-m2repository
  - ./gradlew build jacocoTestReport assembleAndroidTest
  - echo no | android create avd --force -n test -t $ANDROID_TARGET --abi $ANDROID_ABI
  - emulator -avd test -no-skin -no-audio -no-window &
  - android-wait-for-emulator
  - adb shell setprop dalvik.vm.dexopt-flags v=n,o=v
  - ./gradlew connectedCheck
  - bash <(curl -s https://codecov.io/bash)

The actual report upload is done by the bash <(curl -s https://codecov.io/bash) command. Note that this configuration handles both regular unit tests run on the host machine and also instrumentation tests run on the emulator. You should also remember to adjust the Android SDK, build tools and emulator SDK version settings to suit your project’s configuration.

You should also go to your Travis profile page to enable the trigger for the project’s repository so Travis builds it whenever a code change is introduced.

Now, creating a pull request at the project GitHub page should trigger a build. When it succeeds, the codecov-io bot leaves a comment on the pull request with the code coverage information and a link to the project page at Codecov for more details.

Fortunately, Codecov supports uploading multiple report files without the hassle of merging them into one beforehand. If you have a multi-module project or a multi-variant Android project you do not have to take any additional steps here.


There are other solutions that can be used to analyze code coverage reports, provide suggestions and perform checks if the change would degrade the project quality.

Coveralls for example offers similar functionality. Currently, the available coveralls-gradle-plugin expects the user to merge test coverage reports into a single report in a multi-module Gradle project before publishing them to Coveralls.

Probably, the most feature-rich solution is SonarQube™ as it performs static code analysis alongside code coverage reports. It is also possible to feed it with integration test results separately from unit test so it is easier to distinguish them in the end report. The only downside is that it requires self-hosting.

In case of open source and cloud-hosted projects using Codecov may be the most elegant solution at the time of writing this article. If you want to enhance your CI process to get the most out of the code coverage reports then integrating with Codecov may probably be the way to go.

Feel free to comment and ask questions. I will be more than happy to answer them.

You can also find a fully working and up-to-date example on Codecov’s GitHub: https://github.com/codecov/example-android.

Thanks for reading!