Over the last decade, the world of software testing has seen tremendous changes with better and faster technologies being incorporated in it. One such change that has had a huge impact is the inclusion of test automation and it is all thanks to AI (Artificial Intelligence). At first, it was with rule-based test automation and now with the advent of Machine Learning, it has further changed the game.
So, let us help you give an idea of what test automation is in the first place and how AI has had a massive say in enhancing the process.
What is Test Automation?
Whenever the coding of a software is changed, be it to fix a bug, to add a new feature or to improve the overall efficiency of the product, hundreds and thousands of tests are performed before releasing it to the public. However, a huge number of these tests are repetitive and redundant. Performing them manually is quite tiring, which can lead to human errors.
This is where test automation comes into play by automating regression and exploratory tests so that you can focus on other tasks and increase the test coverage. The process of test automation uses various AI rule-based and machine-learning-based tools to find errors and record it in databases while performing such tests.
Importance of Test Automation
Test automation can be used to automate various aspects of software testing like test case creation, regression testing, defect logging and GUI interactions to name a few. With more and more companies switching to Agile developments to develop and test software, the need for continuous testing processes has increased.
Most companies nowadays use Continuous Development (CD) and Continuous Integration (CI) as two of the most common approaches in software testing. All this requires superior efficiency with faster results, which makes test automation an important part of the whole testing scenario. Moreover, companies have integrated AI with test automation to get better and faster results.
By combining AI and test automation, one can now script a hundred tests in 1/1000th of the time what a manual tester would take. In fact, AI can perform other tasks in creation, implementation and execution apart from performing those mundane repetitive tests. This frees up the manual testers to monitor the testing phase and give real-time feedbacks to stakeholders.
At TestUnity, we use AI and test automation to guarantee the best results to our clients.
How has AI changed Test Automation for the good?
In the initial days of test automation, when people were still testing the waters, it was an all rule-based system. However, ever since the inclusion of Machine Learning (ML), AI has significantly changed the game for the good. Here are some of the ways that show the influence of AI and ML on test automation.
Test the UI with Machine Learning-powered visual validation tools
We all know how incredibly important the UI (User Interface) of software or an application is. There was a time when the companies used manual testers to test and observe the UI elements like text size, colour gradients, position of the icons and so on. However, there have been several cases where manual testers could not detect discrepancies in the design.
This is where the inclusion of ML-powered visual validation tools improved the process considerably, as they are able to identify the patterns and spot the errors easily. This approach gives you more reliable results.
Automate API Testing
Application Programming Interface (API) testing is a phase of software testing which checks whether the app meets the expectations for functionality, reliability, security, performance and so on. In a nutshell, it tests whether the software has turned out to be the way it was expected to be or not. As a result, API testing is the core of software testing in current times.
However, performing such an extensive test is quite difficult and to ease the process, ML algorithms provide you with several analytics, which you can use to automate the system. This way, the AI tools perform such tests for different possibilities and that too at a faster rate than manual testers.
Run only the relevant tests
As mentioned above, AI in test automation enables you to not run repetitive tests manually. However, the ML algorithms have changed the process furthermore by having the potential to identify the relevant tests to be conducted and then performing only those tests. For example, if you made a small change in the coding to fix a bug, there is no point of running all the test cases used in the software.
Since ML algorithms are much better at understanding the analytics, they manage to recognise the number of tests that get affected by the change you made. This saves you both time and money.
Create new test cases
AI/ML algorithms can also be used to create new test cases with ease now. Since AI can “read” and analyse the features of the software and subsequently predict the way it should behave, it can build test cases on its own. Machine learning is all about “learning” the software using different algorithms, making it superior at creating tests than the tests created by rule-based test automation process.
Test automation definitely has had a huge impact in the world of software testing but ever since combining AI with it, it became a different ball-game altogether. You can clearly see how impactful it has been to include AI and ML to test automation, thanks to their “reading” and “learning” abilities. If you want to test your software using such efficient practices, get in touch with TestUnity today!
Testunity is a SaaS-based technology platform driven by a vast community of testers & QAs spread around the world, powered by technology & testing experts to create the dedicated testing hub. Which is capable of providing almost all kind of testing services for almost all the platforms exists in software word.