Techniques for Growing Automated Testing Certification Suites

 

Scalable test automation is necessary for modern systems. The use of test clouds, reducing test debt, and intelligent test automation are three techniques that can help you scale your Automation Testing Training ractise and increase efficiency.

Even ten years ago, test automation looked drastically different today. When it comes to the development lifecycle, testing used to be a distinct, compartmentalised activity. The test suite would have a limited number of test cases that would cover all potential end-user scenarios. That quantity is no longer so limited. Test suites must process thousands of tests in progressively less time in order to release new apps to the market. Today's software products are driven by competition in terms of speed and efficiency. A significant role is played by test automation.

Teams of competent software developers that previously spent a lot of time manually writing test scripts are now able to access resources thanks to cloud computing and machine learning technology. This method is no longer scalable for the quantity and complexity of testing required, let alone the expenditures required.

 

1. To take use of the scope and complexity of a virtual environment, use a test cloud.

 

It is a circumstance that both developers and testers are all too familiar with: "It works great on my machine!" Physical devices are subject to environmental variations. As a result, testers encounter a variety of outcomes. Testing on your local PC is far less scalable than testing in the cloud.

The processing speed and memory of your local PC will affect how well the test runs. If your organisation hasn't updated its hardware in a while, it's possible that the several apps and tools you use on a daily basis have caused your computer's CPU or RAM to become overloaded. How would it respond to a scenario with 1,000 test runs? It's likely that it would affect how the application acts during testing or cause the testing process to stop entirely.

Additionally, it takes a lot of time and effort to set up test machines and all of the accompanying infrastructure. It cannot be scaled for current test methodologies.

You can observe how the application being tested operates on a sterile virtual computer free of external disturbances when tests are run in the cloud (for instance, using the Functionize Test Cloud).

Cloud models support the environment's elastic capacity scaling as required by your project. You can use it to run thousands of tests concurrently, increasing the scope and coverage. Additionally, because the resources are handled by the provider, infrastructure maintenance is simple and there are no execution inconsistencies as compared to a local test environment.

 

2. Reduce test debt by using AI and ML.

Due to the pernicious loop of test debt, the majority of testing teams find it difficult to stay up with business needs. The constant upkeep of automated tests is the most significant type of test debt.

Only test execution automation is provided by legacy automated testing technologies. They use selections that are hardcoded. Actual test case programming must be carried out by hand. Some of your automated tests get broken every time your application's user interface is changed, and they must be manually corrected by test engineers or developers before they can be rerun. An programme requires more manual test scripting as it grows larger and more complicated (i.e., as your end users demand additional features and repairs). Testing teams consequently lag behind the rate of new development. They are obliged to painfully trade off quality, release speed, and prices instead of concentrating on jobs that provide value.

We frequently ignore a straightforward indicator that promotes scalability in our pursuit of lightning-fast releases and breathtaking digital transformation. Time.

A vicious loop that wastes the time of limited skilled workers involves fixing tests that break with every update to the programme and then having to correct them again with the following modification. Test the scalability of debt restrictions. No matter what your objectives are for automation, it merely keeps you back.

The future of test automation and the efficient management of test debt is represented by AI, ML, and big data. Functionize leverages big data and machine learning to address test debt. Functionize leverages millions of data points rather than hardcoded static selectors to precisely identify UI elements and track pertinent changes. Tests have the ability to self-heal, so manual scripting is either completely removed or drastically reduced. This helps teams avoid crippling test debt while also enhancing end-to-end test coverage.

 

3. Make use of intelligent test automation to include testing into current DevOps workflows and technologies.

DevOps and CI/CD methodologies are used in modern application development to speed up development, testing, and delivery. These methods began as collaborative and agile methods of development so that features and updates could be released as soon as their constantly expanding customer bases required them. Testing, however, is still lagging behind development's innovation. The bottleneck in the delivery process that test automation frequently becomes is the manual scripting of test cases.

You can get away from this strategy with the use of intelligent test automation. Functionize continuously gathers data and learns more about your application using AI and ML. Your test definition is a dataset that grows with each execution.

In order to maximise the amount of time and computing resources used for tests, Functionize's current testing methodology makes use of functions like Smart Waits, Smart Scrolling, and computer visions. Instead of requiring time-consuming hardcoding of test scripts and substantial, intricate modification, machine learning enables the system to adapt intelligently to change. Your tests self-heal when you update your application, saving you time and allowing you to write more automated tests.

 

You may scale your test automation without being concerned about slowing down the delivery process by integrating intelligent test automation with other contemporary DevOps solutions. You can interface with CI/CD processes if you can scale your functional testing intelligently. Functional tests can run earlier and more frequently if they are relocated to the left. There is no longer any reason to compartmentalise the testing process. Additionally, it significantly lessens the laborious task of repairing fragile test scripts brought on by traditional test automation.

 

For comprehensive test planning and streamlined reporting, an intelligent testing solution also provides test management connectors. Engineering leadership is tasked with innovation and strategy formulation using this integrated methodology. Instead than merely adopting automation for its own sake, it is their responsibility to implement, optimise, and grow it. When used in conjunction with strategic test management, intelligent testing enables them to monitor the development of overall quality, which is especially important in larger organisations for executive reporting.

 

Conclusion

We are entering a time where modern testing technologies can keep up with advancements in software development. The use of test clouds, reducing test debt, and intelligent Automation Testing Certifications are three techniques that can help you scale your automation practise and increase efficiency.