Selecting the right parallel testing tool can be challenging, with so many options available in the market.
Compatibility, scalability, ease of use, analysis and expense are five of the most important details to consider.
Popular parallel testing tools and frameworks include Selenium, JUNit, and Gradle, but like any parallel testing tool or framework, they all are restricted to hardware limitations.
Launchable is the best parallel testing tool helping you bypass hardware restrictions by helping you predict the right tests to run at the right time with Predictive Test Selection.
Software development is faster now than ever, taking the “code fast and break things” mentality to an entirely new level. Which, unfortunately, means that software testing has to keep up with that same pace.
And to do so, teams turn to parallel testing, giving developers a way to run multiple test suites concurrently. This not only speeds up your testing cycle as a whole, but it can also reduce testing time and costs. However, selecting the right parallel testing tool can be challenging, with so many options available in the market.
This post will compare some of the popular parallel testing tools and frameworks, and showcase the benefits and limitations to help you make the most informed decisions when it comes to finding the right parallel testing tool.
There is a vast amount of testing tools out there, each with unique pros and cons. And when it comes to your business, your product, and your team not all tools are fit for the job. Here’s some things we think you should keep in mind as you assess your parallel testing tool.
Evaluate its compatibility. Your project’s tech stack is unique, just like any other project. Ensuring that your chosen testing tool fits within your stack is critical. Otherwise, you could miss out on essential test coverage — or worse, face flaky tests with an ill-fitting config.
See how it scales. Naturally, you’ll want to use a tool that grows with you and your project. Or, you may already have a large project and need something to handle it. You’ll want to make sure that your parallel testing tool can keep up with whatever you throw at it. Otherwise, you could be dramatically impacting your testing.
Ease of use. Your team will benefit significantly from adopting parallel testing, but not if they can’t easily use your tooling. Some tools may have a high barrier of entry — either requiring an entirely new language or confusing and difficult documentation — so you’ll want to stay away from those to keep your testers happy.
Keep an eye on analytic options. Data rules the world, and you’ll want the most insight into your parallel testing processes. Without it, you’ll struggle with your overall efficiency, which nobody wants.
Don’t forget about costs. After the last few years and recent shake-ups within the tech world, every dev team needs to monitor their resources and overall costs. Parallel testing tools are meant to help reduce costs associated with long test runs. But, by its nature, parallel testing can incur expenses with the required hardware to run concurrent tests. If you don’t manage your parallel tests properly (or if the tooling isn’t as efficient as they let on), you’ll be in serious trouble when budgeting for the next sprint.
Parallel testing tools can help teams move toward faster testing cycles, reducing testing time and ensuring that software is bug-free and ready for production. We’ve compiled a list of some of the most popular parallel testing tools and frameworks out there to help you compare what is available and which might work best for your testing team.
Selenium Grid (SG) was created for the specific intent of parallel testing, taking a lot of cues from the original Selenium. It supports most major browsers, operating systems, and major programming languages. Plus, it’s free to use!
The biggest impact Selenium Grid has to do with scalability: it simply doesn’t scale with demand, making it a lengthy process for large projects. Selenium Grid requires teams to enable more node servers to run tests in parallel, but the more nodes there are, the worse your performance becomes. This also affects your overhead costs, as tests will consume more computing power and take longer to complete.
One of the oldest testing frameworks still commonly used today, JUnit has been a beast since its inception. It’s become the de-facto testing solution for Java projects and supports parallel testing out of the box as of version 5.3. However, it’s still considered an experimental feature by the dev team.
With JUnit, you gain access to a powerful testing tool, but you may run into some issues with it still being experimental. JUnit, as a whole, doesn’t handle large test suites well, which makes it a difficult choice for established testing teams. It also doesn’t offer any analytical features and only works with Java, making it a tool with a specific kind of team in mind.
Gradle has the capability to run tests concurrently and supports frameworks including JUnit 5, JUnit 4, and TestNG. Unlike JUnit 5, Gradle can only execute classes of tests in parallel and not individual test methods.
Each test class is added to a single forked JVM worker process. That means that Gradle runs all tests of a particular test class in sequence in a single JVM, excluded from other tests. This complicates things because when your tests share that single JVM, they are now multi-threaded coded and changing defaults can negatively affect all other tests.
Regardless of parallel testing tool or framework, parallel testing as a methodology has limitations. The number of parallel test runs that can be executed is limited by the available hardware resources. Parallel testing requires additional efforts for setting up the test environment, coordinating the test execution, and analyzing the results. This increases the complexity of the testing process, and requires additional resources and expertise.
Every parallel testing tool and framework is missing scalable, reliable test selection intelligence that allows you to only run the necessary parallel tests. Luckily, Launchable has them covered.
Advance Standard Parallel Testing Tools with Launchable’s Predictive Test Selection and Dynamic Subsetting
With its advanced machine learning model, Launchable can analyze your parallel test suite and identify which tests are most likely to fail. Launchable’s Predictive Test Selection dynamically subsets your parallel test suites, so you only run the necessary tests, nothing else.
Reduce the time it takes to run your test suite and get faster feedback on your code changes, all while optimizing the number of tests run on your limited hardware resources with Launchable.
Launchable’s powerful testing intelligence platform is test, language, and tooling agnostic and supports parallel testing out of the box. Get in-depth Test Suite Insights on your parallel tests to monitor your test health and setup personalized Test Notifications via Launchable’s Slack integration so you never have to context switch to check on your parallel test suites status.
Standard parallel testing tools offer the ability to run your tests concurrently, but are limited to resource constraints. Get the most out of any parallel testing tool with Launchable.