A large volume of automated tests (such as 500 tests running for 3 hours) creates bottlenecks, delaying feedback and deployment. The solution is not to reduce tests, but to parallelize them (run them simultaneously) to speed up the process. If you want faster feedback, more efficient pipelines, and a quality strategy that scales, keep reading.
What is test parallelization?
Parallelization means executing multiple tests concurrently, instead of running them one after another. It’s a simple concept.
In the traditional sequential execution, you have:
- Test 1 (5 min) $\rightarrow$ Test 2 (5 min) $\rightarrow$ Test 3 (5 min) = 15 minutes total
With parallelization:
- Test 1, 2, and 3 running concurrently = 5 minutes total
The difference is obvious when laid out this way. Nevertheless, many teams continue to run tests sequentially, either due to a lack of awareness, fear of complexity, or because they have never stopped to question the current process.
Why is Parallelization Important?
The benefits of parallelization go far beyond simply saving time:
- Quick feedback: When tests take 3 hours, the developer who made the commit has already context-switched. If a test fails, they need to mentally return to that code, recall what they were doing, and understand the error. With tests running in parallel, the context is still fresh. Correction happens quickly.
- Enables more tests: There is a practical limit to how many tests you can have when execution is sequential. Parallelization changes this. With distributed execution, adding more tests does not proportionally increase the total runtime.
- Better infrastructure utilization: CI/CD machines are idle for a large portion of the time. You have available computational capacity that is not being used while a single test process runs. Parallelizing means leveraging this existing capacity, especially during off-peak hours.
TestBooster.ai allows you to schedule your tests to run overnight or in the early morning, taking advantage of times when computational resources cost less and do not compete with other activities. You wake up to all the results ready.
How parallelization works
To run tests in parallel, you need multiple “executors” working simultaneously. These executors are called workers, processes, or instances, the name varies depending on the tool, but the concept is the same.
Each worker picks up a piece of the test suite and executes it independently. In the end, the results from all workers are aggregated into a consolidated report.
The most important part is the work division. You need to decide how to distribute the tests among the workers. There are a few common strategies:
- By test file: Each .spec.js or .test.py file runs on a different worker. It’s simple to implement, but it can cause imbalance if one file has significantly more tests than others.
- By class or describe: A more granular division, at the level of individual test suites. It offers better balancing but requires more coordination.
- By estimated time: Workers receive tests based on historical data of how long each one usually takes. A worker doesn’t remain idle while another is working hard. This is the most efficient strategy but also the most complex to implement.
The required infrastructure varies. At a minimum, you need multiple instances running at the same time; these can be Docker containers, virtual machines, or different processes on the same machine. You also need some coordination mechanism to distribute the work and collect the results.
Parallelization in AI platforms (No-Code)
Traditional approaches assume you are writing code, configuring infrastructure, and manually managing workers. There is another way to do this.
In platforms based on Artificial Intelligence and natural language, you don’t need to worry about Selenium Grid, worker configuration, or manual test division. The focus shifts from how to execute to what to test.
In TestBooster.ai, you describe your tests in natural language. Something like: “Validate that the checkout process works with credit card and displays the correct order confirmation.” The platform understands the intent, generates the test scenarios, and executes them in parallel automatically.
Scheduling is also intelligent. You can configure tests to run overnight, after deployments, or during peak hours to validate availability.
Benefits
- Democratization is the first step. Product managers, QAs without a deep technical background, and even business stakeholders can create tests that run in parallel. The barrier to entry drops drastically. You don’t need an entire team to have a robust suite.
- Time savings comes next. Not only in execution (which is already faster due to parallelization), but also in setup and maintenance. You don’t spend weeks configuring infrastructure. You don’t need someone dedicated to keeping the Selenium Grid running.
- Scalability, because you add more tests without worrying about capacity. The platform scales automatically.
- Consistency comes from the fact that variables and flows are reusable across projects. You define once how “logging in” works, and all tests that need it use the same implementation. When something changes, you update it in one place.
Real-life Example
Imagine an average e-commerce site with 50 tests covering different areas: checkout, product search, cart, user profile, wishlist, discount codes.
In the traditional approach, you need to:
- Set up Selenium Grid or similar
- Manually divide tests into balanced groups
- Configure CI/CD to run multiple jobs
- Monitor execution to ensure everything works
- Aggregate reports from different workers
- Maintain this infrastructure over time
With TestBooster.ai, you:
- Describe the 50 scenarios in natural language
- Schedule them to run at 2 AM
- Wake up to a complete report showing what passed and what failed
Parallelization ceases to be a complex technical configuration and becomes a feature. You focus on what you want to ensure, not on how to distribute computational load.
What Are the Next Steps?
Test parallelization reduces feedback time, makes it feasible to have more complete coverage, and improves the utilization of resources you already have.
Approaches vary widely. Traditional tools like Selenium, Cypress, and Playwright require infrastructure setup and worker management. AI-based platforms, such as TestBooster.ai, abstract this complexity and allow you to focus on what to test, not how to distribute the execution.
The choice depends on your context. If you have a large and experienced technical team, traditional tools provide that flexibility. If you want speed without complexity, AI platforms are the way to go.
In any case, the principles are the same: start small, stabilize your tests first, scale gradually, monitor results. Parallelization amplifies both the strengths and the problems in your suite. Fix the foundations before attempting to scale.
If you are not yet parallelizing your tests, start today. Learn from the problems that arise.
Want to implement parallelization? TestBooster.ai centralizes all your tests, with automatic parallelization and intelligent scheduling. Discover TestBooster.ai and transform your quality strategy.
