What are the challenges in AI-driven software testing?

Although AI-based software testing provides automation and efficiency, it has some major difficulties. First, the complexity of producing high quality test data as AI models require a wide range of realistic inputs to identify subtle errors. Second, AI anticipates might be hard to interpret—false positives or missed problems erode trust. Third, AI technologies sometimes require heavy customisation to integrate with existing DevOps pipelines and legacy systems. Fourth, sustaining AI models over time is difficult as software changes and models degrade if not retrained. Finally, there are ethical problems such as the bias of test coverage and data privacy which must be handled carefully to ensure reliable and responsible testing.