How is AI Changing Test Automation
The mobile app domain has evolved and grown beyond bounds in recent times. The industry has grown adaptive of not just the latest tech trends but also the changing principles, like the introduction of Agile in the app development approach.
But there is one inherent process in the holistic mobile app development world which has been slow to these changes — App Testing.
The mobile app testing domain has been taking its own time to adapt to the changing requirements of the world. The waiting stage for the testers has now ended. Automation testing — the principle driven by the addition of AI in quality assurance is now knocking on their doors to not take their job but make them a lot more efficient and productive. Thus posing to be a big advantage of artificial intelligence in testing.
While mass adoption is still awaited, let’s look at how the combination of AI in testing is presently being employed in the testing domain, thus giving more test cases for the uses of AI in testing.
1. Automated Validation UI Testing
Visual testing is that part of the quality assurance process which is intended to verify that the UI appears right to the end users. The idea behind testing UI interfaces is to ensure that every UI element is in the right colours, positions, shape, and size. The intricacy that the process demands is what usually makes it a part of manual testing.
The same intricacy is what makes it the best fit for AI unit testing.
AI in testing, through algorithms will be able to validate the correctness by measuring it against other app screens and the design guidelines active in the market.
2. Testing the APIs
Making a lot of API calls has become one of the most inherent parts of the mobile app development process. Now testing that the APIs work right and that they have been applied rightly, in addition to knowing that they perform as expected under all the different scenarios is also a job that can be well handled by the incorporation of Artificial Intelligence in mobile app quality assurance.
3. Spidering AI
One of the most common applications of AI testing framework in automation can be seen in Spidering. It means using machine learning to write tests for the app by using a spidering method. What happens here is that you point some AI tools at the web app that then starts crawling the app.
As the AI ML testing tool starts crawling, it gathers data that comes from: downloading the HTMLs, taking screenshots, measurement of the load time, etc. By making it a continuous process, the machine is able to build a dataset and train the ML model for the expected app patterns.
This way, whenever the AI based testing tool runs, it compares the present state of your application with the known patterns and if there is a deviation, it flags it as an issue.
4. Increase the Extent of Automated Tests
At present, the mobile app sector is working under the principles of Continuous Integration and Continuous Testing. The principle is known to give birth to a wealth of data which you can use to perform test runs. But finding the time to search the plethora of data to look for common patterns is something that is a challenge for testers working on multiple projects at one time.
AI automation testing is the answer to the classic question “If I make a change in this code, what is the minimum number of tests I will have to perform to know whether the change is good or bad?”
The technology is being used to tell testers with utmost clarity the number of tests that will have to be performed to check the changed code. The tools using AI in test automation will also be able to analyze the present test coverage and the issue areas which have less coverage and even point out the areas where the app is at risk.
The information that the inclusion of artificial intelligence in software testing provides gives the team of QA experts insight into the new test failure in addition to events that they already have to focus on. Additionally, the application of AI in software testing tools will enable them to make the testing process a lot more proactive.
These automation testing ways are only the beginning of the extent of the implications of AI in testing. While these are being used in some percentage already by companies, there is still a lot of scope for their expansion and growth in the future of automated testing.
What can be said with utmost surety is that the time to come is very happening for the testers. Their work is poised to get a lot more productive and efficient as AI will start getting mainstream.
