Month: August 2020

Read Part 1: What is Parallel Testing in Automation?

Here are some of the tips for Successfull Parallel Testing:

Tip 1: 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.

Tip 2: 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.

Tip 3: Avoid Hard-Coding

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.

Tip 4: 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.

Tip 5: 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.

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