From “MOM! Should I keep my raincoat for the field trip?” to “Alexa! Is it going to rain next week?” AI has taken up a lot of space in our lives. Artificial intelligence (AI) stands on two pillars: data and storage. AI is AI because the machine that once used to obey our commands can now store data and use the findings to make its own decisions. Today, we have enough data for every individual through mobile devices that one gets customized ads, diet plans, and hotel recommendations. AI has started to act like oxygen in our lives. We breathe it in and out without even realizing it!
Artificial Intelligence is astonishing. A simple machine that has been fed data aids in making decisions. The basis of this intelligence is the same as when we were children. We first dipped our little fingers in the coffee mug and realized, Oh! It's Hot! And it burns! And it hurts! So, the next time we saw that mug again, we made sure not to go close to it because we remembered how bad it hurts. This simple phenomenon of remembering what elements lead to what events makes AI function and generate results.
AI has hit every industry, and the software industry is no exception. In software testing, AI is being increasingly utilized to support and execute test automation.
The journey from manual testing to automation is in full swing, and test automation offers us a variety of benefits:
- • It identifies defects quickly
- • It can determine the accuracy of software performance
- • It fastens the whole testing process and makes it efficient
- • It eliminates human intervention by taking over repetitive tasks
AI helps in this by smartly recognizing requirements and fetching relevant data and test cases. It can then suggest test case scenarios that best fit the given situation.
Now, remember this work is easy for these AI machines. They can do it quickly and over and over again without complaining. The only thing is that they cannot do this on their own. Why? Because they are unaware of the emotional uncertainty of users and the environment in which the application will be working. For example, in a banking app, the user has to enter a password to do transactions, making it an ideal scenario for the machine. But a human tester might also consider the possibility of security threats such as lost or stolen passwords. Hence, they think of adding another security check, like biometric verification, for transaction confirmation. Therefore, AI will always need manual testers to approve their recommended test cases and think of new and unique scenarios that need to be covered. They are here to assist testers, not to completely wipe them out.
Finally, automation can do a lot more than recommend test cases. Powerful test management tools have the following AI-driven testing:
Automation testing tools
- • Differential testing can identify the updates in a software’s security and code quality.
- • Visual testing assesses the user interface of an application by quantitatively measuring its reliability and usability.
- • Declarative testing examines how the software performs its function
- • Self-healing testing preserves the interface and other components after there are changes in the code
can help run a lot of functional and non-functional tests such as unit, integration, and system testing. These tests seem simple but are very time-consuming. One has to write and run test scripts and test cases that imitate the real user, where the goal is to check that the software can perform in every kind of situation and user input. This automation in test cases does two things to make the testing process smooth. First, AI is proactive, which means as the software scale increases, it takes over the self-healing process preserving the validity of the previous test. Second is the maintenance tasks, where these tests run as required. These tests are simple repetitive task that makes testers pull their hair out. Thanks to automation in test management tools, AI can save testers from going bald.
Although test automation will not be replacing manual testers anytime soon, it is essential for testers to remain updated with the latest trends in automation, including the use of AI. The use of this technology will see an exponential increase in the future, and it is a good idea for organizations to invest in training their teams to work with AI. For example, testers need to learn to choose the right data sets that make the machine learn most accurately about the given software to be tested. They need to oversee the machine’s learning progress and make sure that the emotional, environmental, and security aspect of the software under test is not compromised.
AI in software testing is not a thing of the future, it is already here. Organizations are increasingly adopting AI to support their test automation processes and complement the work of manual testers. However, the use of AI is still in its nascent stages and still has a long way to go to develop to its full ability. With time, it will become more advanced, and we cannot wait to see what this innovative technology will bring in the future!