Why You Should Start with Parallel Testing in Your CI/CD Flow

Parallel testing is a software testing technique that involves running multiple tests at the same time to reduce the overall time required to complete the testing process. When done correctly, running tests in parallel can accelerate the testing process by an order of magnitude and provide feedback on code changes more quickly.

To begin with, parallel testing is not the most fundamental idea. Other testing procedures come first and can be utilized as a foundation to parallelize them afterward. Keeping this in mind, it is advantageous to have a broad picture of how you intend to build your tests. It will be easier to navigate the road if you keep in mind that you want to automate and parallelize your testing. So, to begin, let’s look at the what and why of parallel testing.

The what and why of parallel testing
The name does speak for itself in this case. But before we get there, it is worthwhile to think about what the objective of CICD is. It’s to be able to quickly and efficiently integrate and deploy your codebase to production, especially when working with a large team of software contributors.

Let’s imagine the following scenario: You’ve assembled a sizeable team of software engineers working on a large codebase. Fortunately, you have a version control system in place, such as Git, where you can keep track of all the changes that are made to the codebase, including every time a feature gets developed, a bug gets fixed, or a new branch is made. Afterward, the branch is merged with the mainline, where the code is tested and deployed to production.

This is a process that should be done often to make sure that the mainline remains clean so that the team can continue to work on a clean mainline.

This is a familiar scenario, and it is a common practice in the software industry. The testing phase of the scenario, where the code is being merged with the mainline, is the phase where you should be most interested. It needs to be tested as quickly as possible.

There are several tests that you can run to make sure that your code is working as expected, such as:

  • static code analysis

It’s not an exhaustive list. Each test is done at a specific step in the lifetime of the build and test process, where each step of the software lifecycle is deliberately tested. Not only will you test the most frequently occurring states of the application, but also that the application runs smoothly in the production environment, and that it will behave correctly when the application is interfacing with other applications.

All of these steps can, of course, happen sequentially, where you run all steps one after the other and wait for the result at the end of the pipeline. But as a software engineer, you have multiple instances of servers and CPU cores that you can leverage to accelerate this process. The earlier you know where your software is breaking down during the build phase and testing phase, the earlier you can alert your team member who is responsible for the failing code changes, which translates to faster you can deploy new features for your customers.

Key elements to successful parallel testing

In order for parallel testing to be successful, there are a few key elements that need to be in place:

Test isolation
It is crucial to make sure that tests are isolated from one another before running them concurrently. To phrase it another way, this means that each test should be self-contained and not depend on the results of any other tests. Doing this allows you to run tests concurrently without interference from other tests. One of the steps may involve creating a database and populating it with new data using separate databases or other resources for each test or using techniques like test doubles or stubs to mock out external dependencies.

Test data
To run tests in parallel, you need to have a sufficient amount of test data. You’ll need to create multiple test cases or data sets to ensure that you have enough data to test your code adequately.

Use independent tests
To ensure that tests can be run in parallel without interference, it’s critical to make sure that each test is independent of the others. Each test should have its own set of inputs and expectations and should not rely on the results of any other tests.

Test infrastructure
Parallel testing requires a robust testing infrastructure, including sufficient computing power and resources to run multiple tests at the same time. You may find it helpful to involve a third-party testing platform or set up a dedicated testing environment.

Test management
Use a test management tool or set up a way to track and analyze test results to coordinate the testing process.

Monitor test performance
When running tests in parallel, it’s easy to create bottlenecks or other performance problems in which one of the parallel pipelines takes much longer than the others. Then consider splitting up that single test into smaller pieces to accelerate the whole process. By monitoring test performance, you can identify and address any issues that arise.

Benefits of parallel testing in a CI/CD pipeline

Faster feedback
With larger codebases, it can take considerable time to run tests sequentially. Parallel testing gives you feedback on your code faster, which allows you to identify and resolve issues quickly.

Improved efficiency
By reducing the time it takes to test, merge, and deploy a codebase, developers gain more time to focus on writing new code.

Better utilization of resources
You can use resources like CPU and memory more effectively by running tests simultaneously. As fewer resources are required to complete the same amount of work, this can help lower the overall cost of running tests.

There are a few different approaches to implementing parallel testing in a CI/CD pipeline. One option is to use a tool that allows you to parallelize your tests across multiple machines or virtual machines, which can be particularly useful when working with a large codebase or when running tests that require numerous resources. Another option is to use a cloud-based testing service, such as AWS Device Farm or Google Cloud Test Lab, which allows you to run tests on a wide range of devices and configurations in parallel. Without having to manage the infrastructure yourself, using them makes for a convenient way to scale your testing efforts, with the added benefit of having a convenient interface where you can define a matrix of options and tests to run. They provide a convenient interface where you can define a matrix of options and tests to run.

Limitations of parallel testing

As briefly mentioned above, one of the main limitations of parallel testing is bottlenecks, where one of the tests is taking considerably longer than the rest, thus making the other tests wait for that single test to finish. To avoid this, you can split the test into smaller pieces and run them in parallel.

Another limitation is that CICD providers might have a limit on how many parallel pipelines you can run at the same time. Costs are another potential issue, as parallelized resources are generally more expensive than using a single resource. Before you start parallel testing, you might want to do a cost-benefit analysis to see if it is worth it to use parallel testing in your pipeline.

Your software application might have complex dependencies. For example, you may have a microservice architecture where you have multiple services that communicate with each other. If you are running tests in parallel, you might need to make sure that the services are isolated from each other and that each service can receive and output mock data from other services.

Conclusion

Overall, parallel testing is a powerful way to make a CI/CD pipeline faster and more efficient. By running tests at the same time, you can get feedback faster, improve efficiency, and make better use of the resources you have. You can successfully add parallel testing to your CI/CD pipeline and get these benefits if you follow best practices and use the right tools and methods.

You can learn more about our CI tools at https://nosana.io

Thanks for reading!

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