Accelerate Your CI/CD Pipeline: The DevOps Guide to Parallel Testing

Parallel testing is a software testing approach that involves running multiple tests concurrently to help boost testing efficiency and accelerate CI/CD pipelines. Parallel execution enables DevOps teams to split their test suite into smaller parts, run tests concurrently, and generally speed up the dev-testing process.

The concept of parallel testing is likely familiar to folks across software, DevOps, quality assurance (QA), and product management. Given that 75% of DevOps professionals say more than 25% of productivity is lost to testing, this approach can play an important role for time-strapped DevOps teams. 

Parallel tests can be deployed at any stage in the testing life cycle, and are generally most effective in a few scenarios:

• When testing a large number of cases or configurations,

• When testing is required across multiple environments, and

• When testing legacy systems or multiple versions of software.

Parallel testing isn’t always a perfect solution. There are challenges to implementing parallel testing, such as managing test dependencies and infrastructure costs. However, the benefits typically outweigh the drawbacks – especially when you have the right tools in place. 

Launchable by CloudBees helps DevOps teams leverage AI to adopt a smarter parallel testing approach. The platform allows your team to focus on the right tests, rather than simply running more tests at once.

Let’s dive deeper into parallel testing, and share some of the best practices and tools to maximize its impact.

Parallel testing is a software testing strategy that involves executing multiple test cases simultaneously across different configurations, environments, or platforms. DevOps teams can use parallel testing to divide their test suite into smaller parts, allowing them to run tests concurrently and expedite the testing process.

The goal of parallel testing is to remedy time constraints by distributing tests across available resources. Let’s say 20 test cases typically take 100 minutes to complete with traditional sequential testing. Parallel testing could break those 20 tests into 10 groups and execute them in parallel (by executing them on different machines), reducing the total testing time to just 10 minutes.

Parallel testing empowers faster feedback and enables teams to maintain agility by catching issues earlier in the software development lifecycle.

Parallel and sequential testing differ primarily in how test cases are executed. During sequential testing, tests are run one after another, meaning each test must be completed before the next begins. Parallel testing boosts efficiency by allowing teams to execute multiple test cases at the same time. 

Both software testing methods can be useful, depending on the goals and budgets of a project. Sequential testing may be simpler to implement. However, parallel testing distributes the workload, allowing teams in agile or CI/CD environments to achieve greater efficiency and faster feedback.

The benefits of parallel testing include:

• Accelerated feedback cycles

• Enhanced platform and hardware coverage

• Scalability

Parallel tests are particularly useful if you’re testing a large number of test cases or configurations. QA teams can run tests faster, allowing them to provide faster feedback to DevOps teams. Quality assurance pros can utilize Launchable’s Intelligent Test Failure Diagnostics feature to help process these failures faster.  

Typically, parallel tests lend themselves to testing on different operating systems, platforms, and devices. This versatility allows DevOps teams to spread their test suits across various devices without sacrificing quality.

Operating with a parallel testing approach allows teams to add more machines to execute tests simultaneously. The end result is testing more environments while maintaining speed, thus increasing the scale of your project. 

A faster, more robust, and less expensive testing process is certainly a major benefit. However, there are some potential downsides and limitations to the parallel testing approach.

As parallel testing spreads tests across multiple environments and machines, test synchronization can become an issue. This is especially true as test suites scale and become more expansive; it can be difficult to ensure that each device runs with the correct package and software versions.

QA and DevOps teams often face a firehose of test failures they need to triage. Once again, this is an area where Launchable’s Intelligent Test Failure Diagnostics features can come in handy. 

Managing different operating systems, devices, or browsers in parallel can add layers of complexity to the testing process, especially if certain configurations are not properly isolated or there’s a lack of consistent environments. Developers could chase perfection, creating a knot of difficulties that could be hard to untangle. Alternatively, teams without sufficient product knowledge may quickly write tests that will fail in other environments. 

Looking for the best way to implement parallel testing in your DevOps org? Or perhaps you’re in search of some new strategies to enhance your parallelization efforts. 

Here are three best practices for parallel testing:

• Isolate tests to ensure independence

• Use parallel-friendly frameworks

• Utilize containerization and virtualization

Each test should remain independent of others, and shouldn’t rely on shared data or external stats (which could trigger conflicts when run in parallel). Test doubles, such as mocks and stubs, can isolate dependencies and help reset shared resources before each test execution.

Certain testing frameworks—pytest-xdist for Python, JUnit with Maven Surefire for Java—natively support parallel tests. They’ll make configuring and managing parallel execution much easier. It’s important to research the capabilities of your testing framework before deploying it.

Containers such as Docker can help isolate environments for each test. This affords each test its own environment to reduce the risk of interference between parallel tests. DevOps and QA teams should set up their test infrastructure to produce independent containers (or virtual machines) for every test suite.

As with nearly every aspect of DevOps, artificial intelligence and machine learning are affecting the way we think about parallel testing. Here are a few examples:

An AI-based test intelligence platform, such as Launchable by CloudBees, allows you to bring the latest advancements in AI to your parallel testing approach. Even in parallel, running your entire test suite isn’t efficient. Launchable’s dynamic subsets allow teams to run the most relevant tests for a code change. The platform then uses machine learning to decipher the tests most relevant to the code change and then creates a dynamic subset of the most valuable tests to run.

Not all tests are equally important or likely to fail. A tool such as Launchable's Predictive Test Selection allows you to prioritize which tests to run in parallel based on factors such as code changes or historical test data. This ensures that the most relevant tests are run first, improving efficiency and reducing resource consumption.

Traditionally, triaging and analyzing test failures was a manual process that slowed down the test workflow. Technology such as our Intelligent Test Failure Diagnostics offers an  AI-augmented approach to help QA reach root causes faster.

The market is flooded with parallel testing tools. Selecting the best one for your organization can be difficult, so we’ve made a quick shortlist: 

Launchable by CloudBees is built to help reduce the time it takes to run your test suite, generate faster feedback on code changes, and optimize the number of tests run on your limited hardware resources. The platform is test, language, and tool agnostic—helping support parallel testing out of the box.

Selenium Grid is free to use, and it supports most major browsers, operating systems, and major programming languages. It provides a simple way to run tests in parallel on multiple machines. However, it typically doesn’t scale with demand, creating a lengthy testing process for larger projects.

JUnit is one of the OG testing frameworks, and it supports parallel testing out of the box with its 5.3 version. It is, by nature, an experimental feature by the dev team, meaning users may run into issues due to its lack of scalability and analytical features.

Gradle, another open-source developer tool, runs tests concurrently and supports frameworks such as JUnit and Test NG. While effective, it can only execute classes of tests in parallel, not with individual test methods. 

Parallel testing is a powerful approach that helps DevOps teams reduce test times, increase test coverage, and improve the efficiency of CI/CD pipelines. 

Having the right tools at your disposal can help you maximize your parallel testing efforts. 

Chat with an expert to see how Launchable can help your team leverage AI for test suite optimization.