CI¶

QEMU has configurations enabled for a number of different CI services. The most up to date information about them and their status can be found at:

https://wiki.qemu.org/Testing/CI

Definition of terms¶

This section defines the terms used in this document and correlates them with what is currently used on QEMU.

Automated tests¶

An automated test is written on a test framework using its generic test functions/classes. The test framework can run the tests and report their success or failure [1].

An automated test has essentially three parts:

The test initialization of the parameters, where the expected parameters, like inputs and expected results, are set up;
The call to the code that should be tested;
An assertion, comparing the result from the previous call with the expected result set during the initialization of the parameters. If the result matches the expected result, the test has been successful; otherwise, it has failed.

Unit testing¶

A unit test is responsible for exercising individual software components as a unit, like interfaces, data structures, and functionality, uncovering errors within the boundaries of a component. The verification effort is in the smallest software unit and focuses on the internal processing logic and data structures. A test case of unit tests should be designed to uncover errors due to erroneous computations, incorrect comparisons, or improper control flow [2].

On QEMU, unit testing is represented by the ‘check-unit’ target from ‘make’.

Functional testing¶

A functional test focuses on the functional requirement of the software. Deriving sets of input conditions, the functional tests should fully exercise all the functional requirements for a program. Functional testing is complementary to other testing techniques, attempting to find errors like incorrect or missing functions, interface errors, behavior errors, and initialization and termination errors [3].

On QEMU, functional testing is represented by the ‘check-qtest’ target from ‘make’.

System testing¶

System tests ensure all application elements mesh properly while the overall functionality and performance are achieved [4]. Some or all system components are integrated to create a complete system to be tested as a whole. System testing ensures that components are compatible, interact correctly, and transfer the right data at the right time across their interfaces. As system testing focuses on interactions, use case-based testing is a practical approach to system testing [5]. Note that, in some cases, system testing may require interaction with third-party software, like operating system images, databases, networks, and so on.

On QEMU, system testing is represented by the ‘check-avocado’ target from ‘make’.

Flaky tests¶

A flaky test is defined as a test that exhibits both a passing and a failing result with the same code on different runs. Some usual reasons for an intermittent/flaky test are async wait, concurrency, and test order dependency [6].

Gating¶

A gate restricts the move of code from one stage to another on a test/deployment pipeline. The step move is granted with approval. The approval can be a manual intervention or a set of tests succeeding [7].

On QEMU, the gating process happens during the pull request. The approval is done by the project leader running its own set of tests. The pull request gets merged when the tests succeed.

Continuous Integration (CI)¶

Continuous integration (CI) requires the builds of the entire application and the execution of a comprehensive set of automated tests every time there is a need to commit any set of changes [8]. The automated tests can be composed of the unit, functional, system, and other tests.

Keynotes about continuous integration (CI) [9]:

System tests may depend on external software (operating system images, firmware, database, network).
It may take a long time to build and test. It may be impractical to build the system being developed several times per day.
If the development platform is different from the target platform, it may not be possible to run system tests in the developer’s private workspace. There may be differences in hardware, operating system, or installed software. Therefore, more time is required for testing the system.

References¶

[1]	Sommerville, Ian (2016). Software Engineering. p. 233.

[2]	Pressman, Roger S. & Maxim, Bruce R. (2020). Software Engineering, A Practitioner’s Approach. p. 48, 376, 378, 381.

[3]	Pressman, Roger S. & Maxim, Bruce R. (2020). Software Engineering, A Practitioner’s Approach. p. 388.

[4]	Pressman, Roger S. & Maxim, Bruce R. (2020). Software Engineering, A Practitioner’s Approach. Software Engineering, p. 377.

[5]	Sommerville, Ian (2016). Software Engineering. p. 59, 232, 240.

[6]	Luo, Qingzhou, et al. An empirical analysis of flaky tests. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. 2014.

[7]	Humble, Jez & Farley, David (2010). Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment, p. 122.

[8]	Humble, Jez & Farley, David (2010). Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment, p. 55.

[9]	Sommerville, Ian (2016). Software Engineering. p. 743.

Custom CI/CD variables¶

QEMU CI pipelines can be tuned by setting some CI environment variables.

Set variable globally in the user’s CI namespace¶

Variables can be set globally in the user’s CI namespace setting.

For further information about how to set these variables, please refer to:

https://docs.gitlab.com/ee/ci/variables/#add-a-cicd-variable-to-a-project

Set variable manually when pushing a branch or tag to the user’s repository¶

Variables can be set manually when pushing a branch or tag, using git-push command line arguments.

Example setting the QEMU_CI_EXAMPLE_VAR variable:

git push -o ci.variable="QEMU_CI_EXAMPLE_VAR=value" myrepo mybranch

For further information about how to set these variables, please refer to:

https://docs.gitlab.com/ee/user/project/push_options.html#push-options-for-gitlab-cicd

Here is a list of the most used variables:

QEMU_CI_AVOCADO_TESTING¶

By default, tests using the Avocado framework are not run automatically in the pipelines (because multiple artifacts have to be downloaded, and if these artifacts are not already cached, downloading them make the jobs reach the timeout limit). Set this variable to have the tests using the Avocado framework run automatically.

AARCH64_RUNNER_AVAILABLE¶

If you’ve got access to an aarch64 host that can be used as a gitlab-CI runner, you can set this variable to enable the tests that require this kind of host. The runner should be tagged with “aarch64”.

S390X_RUNNER_AVAILABLE¶

If you’ve got access to an IBM Z host that can be used as a gitlab-CI runner, you can set this variable to enable the tests that require this kind of host. The runner should be tagged with “s390x”.

CENTOS_STREAM_8_x86_64_RUNNER_AVAILABLE¶

If you’ve got access to a CentOS Stream 8 x86_64 host that can be used as a gitlab-CI runner, you can set this variable to enable the tests that require this kind of host. The runner should be tagged with both “centos_stream_8” and “x86_64”.

Jobs on Custom Runners¶

Besides the jobs run under the various CI systems listed before, there are a number additional jobs that will run before an actual merge. These use the same GitLab CI’s service/framework already used for all other GitLab based CI jobs, but rely on additional systems, not the ones provided by GitLab as “shared runners”.

The architecture of GitLab’s CI service allows different machines to be set up with GitLab’s “agent”, called gitlab-runner, which will take care of running jobs created by events such as a push to a branch. Here, the combination of a machine, properly configured with GitLab’s gitlab-runner, is called a “custom runner”.

The GitLab CI jobs definition for the custom runners are located under:

.gitlab-ci.d/custom-runners.yml

Custom runners entail custom machines. To see a list of the machines currently deployed in the QEMU GitLab CI and their maintainers, please refer to the QEMU wiki.

Machine Setup Howto¶

For all Linux based systems, the setup can be mostly automated by the execution of two Ansible playbooks. Create an inventory file under scripts/ci/setup, such as this:

fully.qualified.domain
other.machine.hostname

You may need to set some variables in the inventory file itself. One very common need is to tell Ansible to use a Python 3 interpreter on those hosts. This would look like:

fully.qualified.domain ansible_python_interpreter=/usr/bin/python3
other.machine.hostname ansible_python_interpreter=/usr/bin/python3

Build environment¶

The scripts/ci/setup/build-environment.yml Ansible playbook will set up machines with the environment needed to perform builds and run QEMU tests. This playbook consists on the installation of various required packages (and a general package update while at it). It currently covers a number of different Linux distributions, but it can be expanded to cover other systems.

The minimum required version of Ansible successfully tested in this playbook is 2.8.0 (a version check is embedded within the playbook itself). To run the playbook, execute:

cd scripts/ci/setup
ansible-playbook -i inventory build-environment.yml

Please note that most of the tasks in the playbook require superuser privileges, such as those from the root account or those obtained by sudo. If necessary, please refer to ansible-playbook options such as --become, --become-method, --become-user and --ask-become-pass.

gitlab-runner setup and registration¶

The gitlab-runner agent needs to be installed on each machine that will run jobs. The association between a machine and a GitLab project happens with a registration token. To find the registration token for your repository/project, navigate on GitLab’s web UI to:

Settings (the gears-like icon at the bottom of the left hand side vertical toolbar), then

CI/CD, then

Runners, and click on the “Expand” button, then

Under “Set up a specific Runner manually”, look for the value under “And this registration token:”

Copy the scripts/ci/setup/vars.yml.template file to scripts/ci/setup/vars.yml. Then, set the gitlab_runner_registration_token variable to the value obtained earlier.

To run the playbook, execute:

cd scripts/ci/setup
ansible-playbook -i inventory gitlab-runner.yml

Following the registration, it’s necessary to configure the runner tags, and optionally other configurations on the GitLab UI. Navigate to:

Settings (the gears like icon), then

CI/CD, then

Runners, and click on the “Expand” button, then

“Runners activated for this project”, then

Click on the “Edit” icon (next to the “Lock” Icon)

Tags are very important as they are used to route specific jobs to specific types of runners, so it’s a good idea to double check that the automatically created tags are consistent with the OS and architecture. For instance, an Ubuntu 20.04 aarch64 system should have tags set as:

ubuntu_20.04,aarch64

Because the job definition at .gitlab-ci.d/custom-runners.yml would contain:

ubuntu-20.04-aarch64-all:
 tags:
 - ubuntu_20.04
 - aarch64

It’s also recommended to:

increase the “Maximum job timeout” to something like 2h

give it a better Description