Margarita Simonova is Founder and CEO of ILoveMyQA.com. Read Margarita Simonova's full executive profile here.
was discovered in 1947, it was quite literally a moth that had become trapped inside a system at Harvard University that was disrupting the electronics. At that time, the concepts of debugging and testing were indistinguishable. But in 1957, testing became its own separate concern as it looked into how an application performed in real-world scenarios.
There are several advantages, though, to using traditional automated testing. One advantage is that there are already established methods. That means that testers already know what they have to do to successfully test software. In addition, there is a lot of community support. If a tester runs into an issue, it is likely that they can ask the community and others will have had similar issues that they can give advice on.
As with traditional automation testing, there are also downsides to AI-driven testing. One disadvantage is its complexity. When an AI model gives undesired results, it can be very difficult to troubleshoot because AI is a bit of a “black box.” Another issue is its higher initial cost. It can take a lot of resources to properly train the AI, and the training materials need to be expertly selected. One further issue is the specialized skills required for AI-driven testing.
In terms of resources, traditional automation testing requires small teams of experts. They need to be skilled in various programming languages as well as automation frameworks. For AI-driven testing, a larger team may be needed initially to build up the data set and work with the AI. They will need to be familiar with both automation processes as well as AI.
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