Artificial intelligence is increasingly visible in many fields, and so it is in quality assurance. AI is often presented as the perfect solution for every problem, but is that true? I believe everyone has to give credit to the power of AI in image analysis and unit/API/UI test automation. However, it is worth keeping in mind that this field of study is still far from reaching perfection.
Currently, AI can resolve only specific problems, such as increasing test coverage levels in unit tests. AI testing automation has the potential for some companies to limit unnecessary costs in the supervision of software solutions and products.
Besides cost reduction, AI in automation testing provides benefits like eliminating lone errors, which are more likely to escape the tester's eye.
How can you use the potential of AI test automation in 2022?
- Visual testing to check UI design quality.
- Spidering - AI collects the data based on continuous website visits, so it can compare it later on for potential downgrades. It still needs a human touch in the end though.
- For now, AI cannot provide full test coverage automatically. The current test production process is based on training on a specific dataset, with the test cases being generated later, with checking required by the tester. However, AI systems could take some of the testing load, reducing a lot of the repetitive work.
There are many reasons why this tech is predicted to be worth 310 billion dollars by 2026, and those mentioned above are only a few of them.
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