Although automation of tests based on AI has advantages, there are challenges. Infrastructure, trained resources and data governance strategies are necessary to fully rely on AI capabilities. Having AI models formed by high quality data guarantees that predictions would be accurate and that automation work better. Culture here to adopt AI in test automation encourages collaboration between AQ engineers, AI developers and professionals.
In conclusion, Srikanth Kunchaparthy Maintains that the AI and the ML transform the arena of software tests on bases of efficiency, flexibility and precision. Less labor can be used, guaranteeing better quality and faster development. Automation based on AI makes tests faster and reliable; Therefore, the definition of a completely different quality and innovation reference in the digital world.
