The life cycle of web development is divided into several key processes. It starts with conceptualization before it goes to production. Once production is done, it undergoes rigorous testing. This step takes the most time.
In recent years, automated testing became a staple among development teams. Using it cuts down the testing time frame significantly. Testing became more accurate too.
Artificial intelligence makes test automation smart. Machine learning, in particular, is integrated into test automation because of its ability to follow and recognize patterns. Test automation is run by patterns. If the algorithm has an anomaly, the testers weed it out and replace it with the correct data. Machine learning will do just that.
More than recognizing patterns, there are several ways on how to use AI in Test Automation.
Let’s dive into them.
Uses of AI in Test Automation
Visual Automated Testing
Automated testing before fully integrating with artificial intelligence was focused on the back-end codes. It was focused on the foundation, often overlooking how these codes appear to users. With the advances in machine learning, visual automated testing is now possible.
WIth visual automated testing, the AI sees the interface like a regular user but with keener observation capabilities. It can easily distinguish mistakes that normal users overlook. It makes sure that the UI experience is excellent.
The automated test sees to it that all the elements are in their proper places. Moreover, it also sees to it that all the elements are in their proper sizes, colors, and other visual features.
Along with the innovation of visual testing, test automation with AI makes self-healing automation possible. With self-healing automation, the test goes above its usual results of pointing out anomalies. It corrects the code immediately without further instructions from the developer.
From the name itself, the code heals itself to come up with a clean and runnable output. However, there is an extent to what it can heal or correct. It is limited to correcting most UI elements. UI components mainly include navigational tools, such as arrows, sliders, and search bars.
A visual self-healing automation can clean the interface of your website with little or no supervision from a developer. The hours usually spent for manual visual testing alone can be channeled to the other facets of mobile app testing or in the web development life cycle as a whole.
Calculates the Optimal Number of Tests
Time is always a necessity for testing. The complete cycle of web development usually takes up the most time because it requires repetitive test cycles. The repetition is said to help with making sure that there will be no more bugs once the code is released to the market.
But with AI now integrated with test automation, the number of test runs will be limited. Naturally, the limit will be until the bugs are fixed. When done manually, the developer can never be 100% sure that all the bugs are corrected. On the other hand, test automation will run it endlessly until acted on by a developer.
In the case of automated tests with AI, the runs will stop when the code has completely healed itself. This feature also avoids problems of collecting overwhelming data that has little or no bearing with the current output. Endless test runs result in a pile of data results.
Aside from calculated tests, integrating AI in test automation results in focusing on tests that matter. Machine learning analyzes the code and flags the areas that lack coverage. It also flags areas with problematic coverage. These tools also help in risk assessment and management by pointing out possible risk areas.
Declarative testing is often referred to as a new paradigm in software testing. In this method, the web developer declares the intention of the code in its natural language. The system now has the responsibility to carry out the intention of the developer. It sounds surreal, but it is possible with artificial intelligence.
In declarative testing, the automation code into three components. These are the Answer, Executor, and Verifier. Each component signifies a process in creating and checking the solution that the AI comes up with.
Aside from that, the classification into different components makes it easier for the test automation to keep up with the developments of the software as it upgrades.
This testing approach, together with self-healing automation, is inclined towards perfecting the user interface.
The use of machine learning in test automation also paved the way for differential testing. Similar to how test codes learned how to self-heal, differential testing learns from timely and regular feedback. But, the feedback is related to the classification.
Differential testing, per se, is the go-to testing method for deep learning systems. It is an effective method that makes use of derivatives. With this method, it can create training algorithms that can learn function approximations. These function approximations, in turn, help correct the bugs in the system.
As it progresses, these accurate approximations eradicate the need for cross-testing or cross-validation. The test learns from the feedback from different datasets. It classifies the differences thus, honing the function approximations better.
It is known to work well with smaller data sets now. But as it is undergoing constant testing, this will become one of the testing staples in the following years.
Artificial intelligence, particularly machine learning, is continuing to transform test automation for the better. It banks on the innovative nature of test automation and is making it better than expected. Although the advancements are mostly focused on UI testing, these improvements will trickle into user experience testing later on.
From self-healing and self-learning tests, the innovation of testing is easing away from its boundaries. Moving forward, we might actually fully automate testing with little supervision from the developers. The developer’s main role will completely focus on the production and leave the testing to the bots.
It might be a long shot for now, but it’s definitely possible.