5 simple steps for Component testing using Docker
Component testing is an important protection against regression errors. After every change to your component, you should test its public interfaces in isolation from the environment it runs in. In classic OTAP setups, this can be a pain, but using Docker, you can avoid many of the problems by creating a dedicated environment, just for the occasion.
Our test strategy consists of just 5 simple steps for component testing, that are fully automated using a Jenkins build server:
1) Perform unit test after compiling your code
During development, it’s important to get quick feedback on errors in your code. The GUI you use is the first layer, and the most direct protection. It protects against syntax errors. The second layer is the unit test. It should verify that your code has no erroneous constructions, like breaking on empty lists and other out of bounds exceptions. It should focus on the technical details of your implementation. It should test the constructions in your code, but take care your are not testing the libraries and such that you are using. Libraries have their own test suites, and duplicating these tests will not add any value.
If your are using a code-quality gateway, such as SonarQube, it should be invoked just after the unit-tests. A gateway will improve the quality of the entire project by enforcing code standards, unittest coverage and by preventing architectural debt in your code. It reduces the burden of peer-reviews by automating the bulk of the review work, leaving only the interesting work to the developers.
2) Create a docker image of your component, as usual
Once you have passed your unit tests, you should create a docker image of your component, ready for deployment in the environment. This is a candidate image for production, and it will not be changed anymore. Whenever it passes a test-phase, it will be promoted to a next environment. This means that our docker image needs to be configurable for different environments, but the executable inside together with the internal structure of the docker image must be final.
3) Create a docker image from the image of step 2, and add mocks and settings
The image created in step 2 is final, but Docker allows us to derive from an image and add extra components. We run an application server in our docker image, where the component is deployed. In the same application server, we can deploy our mock services. All external api’s used by our component are mocked using the same platform as our component itself. This is important, because when using Docker, you should have only one executable running per container. This executable should perform the role of both the component as the mocked services. It also simplifies things, because the developer needs only one skill-set instead of two: the application and the mock framework.
The configuration for our component is also added to this image, so that it connects to the mocked api’s out of the box instead of the external api’s. The docker image needs no further configuration, and is ready to respond to our test messages directly after spinning up.
4) Deploy the image of step 3, and run your component tests against your mocked component
Our component uses one dependency that is hard to mock: the database. This can be circumvented by creating a dedicated database per test. Again, Docker shows its strengths, as we can just spin up a Docker database image together with our component test image. This implies that our component must be able to create it’s own database structure, or that we have a database image with the predefined structure available. We use the former.
Now that our component is running together with it’s mocks and database dependencies, we can initiate the component test suite from Jenkins. All tests are run in isolation, on the just created stand-alone environment, and results are gathered.
The things we verify in the component test phase are functional, and can be written down using the following format: given that the mocks provide certain data, when I call the provided public api of my component, then I expect a certain result. For example: given that a customer X is returned from the customer mock service, when I call the order service to create an order for customer X, the result should be that an order is created.
A good practice is to write down small scenario’s of business events and bundle each scenario in a testcase. Testcases should be independent of eachother, so you shouldn’t use database data stored in one scenario to execute the next one. The only dependencies are between steps inside a scenario, where you can create something, read it, update it, etc. This way can can choose the order of the scenarios and perhaps limit your testing to one case when you try to reproduce an issue.
5) Proceed to deploy the image of step 2, and perform integration and system tests as usual
Once the component testing is successful, the component test environment is deleted, since it isn’t needed anymore, and it should be newly created before every test.
We take the base image we created in step 2 and deploy it on our integration test environment.
Some points to take away
- Our component is able to create its own database structure from scratch, so we can start with an empty database every time.
- We use an application server to host both the component and the mock services
- We build a mocked docker image on top of our production-ready image
- Jenkins is used to create and destroy the docker environments
- Docker compose can be used to create an environment, but specialized products such as Kubernetes or OpenShift make life for a developer much easier.
- Component testing can seem expensive, but the longer the software lives, the more value is returned from component tests. Don’t skip out on the tests, but make implementing tests easier.