The continuous integration and delivery methodology is a great way to keep you releasing high-quality products in the shortest possible time. All of this relies on continuous testing and requires a faster test cycle. While the traditional testing methodology – sequential testing – means a long queue of test jobs and longer time-to-market, parallel testing is the favored approach for higher test efficiency and quicker turnaround time in software deliveries.
What is Parallel Testing?
Parallel testing is an automated testing process that developers and testers can launch multiple tests against different real device combinations and browser configurations simultaneously. The goal of parallel testing is to resolve the constraints of time by distributing tests across available resources.
For example, if 20 test cases take a total of 100 minutes to complete, then 10 parallel execution could run 2 test cases each and bring the total testing time down to 10 minutes. Ideally, if you have sufficient resources, say 20 real mobile devices and real browsers for simultaneous execution of 20 test cases, then you’ll be able to significantly shrink the runtime to 5 minutes.
On the other hand, sequential testing or serial testing is the opposite of parallel testing. In the above case, 20 test cases are executed one after another, and you will need to waiting 100 minutes for the test results.
Benefits of Parallel Testing
As we have scratched the surface of what parallel testing is and how it influences the test cycle, there are a couple of more reasons why you should consider testing in parallel.
Sequential testing is time-consuming, while parallel testing will allow you to divide invested time by the number of environments. In order to test your application against ten devices, all you need to do is write only ONE script and run it against all your target devices, cutting your testing time by ten times.
The building, maintaining and keeping your own test environment up-to-date can burn a hole in your pocket. When it comes to parallel testing, maintenance isn’t a headache anymore — in fact, you lease the needed testing environment, always updated. Plus, cloud-based testing grids allow you to run tests at high concurrency, making the cost per test significantly lower.
It’s always a good idea to run your application through as many platform-device-browser combinations as possible so that no bug sneaks in. Parallel testing will take your test coverage to the next level, giving you a significant ROI boost.
Optimization of Your CI/CD Processes
Parallel testing is the best friend of continuous integration and delivery. By testing in parallel, you can run tests as soon as developers submit new code updates throughout the entire SDLC. Timely reporting and quick feedback in parallel testing will also facilitate better communication between various departments.
Improvement of Testing Practices
Parallel testing improves the QA routine in your company. The reason is crystal clear: by testing at high speed, you can test more. This gives your QA team a chance to improve their testing practices and pinpoint bugs faster.
Gradual Transition from Sequential Testing
Transitioning your QA regime from sequential to parallel testing is a huge undertaking if you try to do all at once. Starting small might be your best bet. You can begin with the test cases that are most adaptable to the parallel testing environment, giving your engineers enough time to adjust the rest of the tests.
Tips for Successful Parallel Testing
Many companies switched to parallelism only in some instances and avoid parallelizing all their QA processes. Why? Infrastructure limitations, data dependencies, poor test cases data management and hard-coding are the most common constraints. The good news is there are ways to nail them down and reap all the benefits of it properly.
Test in the Cloud-Based Environment
Implementing a parallel testing strategy using in-house resources is one of the most typical mistakes. Building and maintaining your own infrastructure is not efficient. It is not just time- and cost-consuming – you won’t be able to make the most of parallel testing where the ability to test at high concurrency is a major advantage. Also, keeping your testing environment up-to-date requires a lot of resources. To this end, it’s a good idea to turn to cloud-based services that allow you to access the necessary devices at any time.
Avoid Dependencies Between Tests
Dependencies between different test cases are a primary reason why transitioning to parallel testing is so challenging for many teams. Simply put, when test cases are dependent on each other, you should run them in a particular order, which can destroy any parallel testing strategy. So, it is critical to creating your test cases to be atomic, independent from each other. Only then, you will be able to run them at any time and in any order, making your testing processes free of constraints.
Hard-coding is embedding data directly into the source code instead of generating it at runtime. This notion is an enemy of efficient parallelization since it creates dependencies between test cases, mentioned above. It is strongly recommended to avoid hard-coding values when scripting your test cases to ensure that each of your tests is self-sufficient and can be run whenever necessary. Use the data-driven approach to configure your test cases when they are running in parallel.
Manage Test Data Efficiently
Efficient test data management is the key to successful parallel test execution. But first, you need a strategy. There are two components you might want to add to it: a creational strategy (which creates test data needs) and a cleanup strategy (which cleans it up). Still, the only thing that matters is that your strategy is adapted to your particular case. The following ones are quite basic, and perhaps that’s what makes these data management strategies so effective:
- elementary approach, which has no creational and cleanup strategies
- refresh-your-data approach, which resets your source code in between test executions but has no creational strategy
- selfish data generation approach, which has a creational strategy but no clean up one.
These are the most basic strategies. You can mix and match them to serve your own case or explore alternatives, such as generating other data or refreshing specific data.
Build Tests to be Run in Parallel
You don’t want to end up with a bunch of tests that can’t be run in parallel right before the release date. Planning correctly from the get-go will save you from nasty surprises when time’s up or when a deadline is due. Create your test cases with parallelization in mind should be your way to go. And believe us, it sounds harder than it actually is: test cases created for running in parallel are simpler, shorter and faster to build.
Parallel testing can save your team from delays in delivery without compromising the quality, which makes it superior to sequential testing.
With parallelization, you can cut your QA expenses, run cases at high concurrency, optimize your CI/CD processes and constantly improve your scripts to get more accurate results. At the same time, all these advantages are out of reach without a correct strategy. Testing in a cloud-based environment, avoiding hard-coding, eliminating data dependencies and scripting with parallelization in mind will help you enjoy all the benefits of parallel testing.