Artificial Intelligence (AI) has greatly evolved and contributed to various areas of technological advancements. Over the past few years, researchers have utilized AI to improve software testing a great deal. Although there are still many areas where AI’s full potential isn’t applied. So far, we can see AI becoming a part of our daily engineering process. However, it is important to see how it contributes to the software testing process in the future. QA teams face various problems almost daily and waste their time on finding solutions. Although they have many tools and processes developed to enhance their software development process. Even when AI is introduced to current testing processes, the codes that have already been through the testing process may stop working. This can be quite daunting for the QA teams. Thus, they need to explore more possibilities in improving software quality along with speeding up the delivery process.
So each time a development team changes the existing code or updates it, they are required to carry out new tests. Regression testing takes a lot of time, yet the manual testing efforts leave QA experts puzzled. With more and more complex software development processes, testers need to add their feedback and evaluation to the development teams. Thus they also need to work smarter than ever before. In the past, development teams were supposed to deliver releases only once or twice a month. This count has now been reduced to a weekly basis, which needs more extensive software testing solutions. This is why AI is being leveraged in software testing tools, making them more effective and efficient.
By improving the use of AI in the testing tools, the team of testers can work more efficiently and go above and beyond the traditional manual testing models. With the help of the continuous testing platform, AI can recognize these changed controls better than humans. It also supports the constant updates to AI’s algorithms and with automation, even the slightest changes can be observed. Moving forward into automation testing, AI is being widely used for various user interfaces. The recognized controls are recognized when the tools are created and the testers can use the controls in these setups. So once this hierarchy of controls is observed, tests can create technical maps such that AI uses the Graphical User Interface (GUI) to achieve various labels for the different controls.
So this is how AI can do more wonders into software testing and make the software development lifecycle more interesting for the software testers. Organizations are supposed to produce greater testing solutions and lower the cost of introducing AI into test automation. Saving this money can be useful for covering quality assurance where it comes to exploratory testing or other parts of the software testing process. Let’s hope that software testing experts will introduce AI with more economical solutions so that software testing companies can utilize these solutions and make the most of their testing efforts.