What are the main challenges of using AI in quality assurance?

What are the main challenges of using AI in quality assurance?

Some key challenges include data quality, integration with existing systems, and the need for human oversight.

How does data quality affect AI in QA?

Data quality is crucial because AI models rely on accurate and representative data to make reliable assessments.

Can AI completely replace human QA testers?

While AI can automate many tasks, human testers are essential for nuanced decision-making and creativity.

What role does machine learning play in QA?

Machine learning helps in identifying patterns in data, predicting defects, and improving testing processes over time.

What are some best practices for implementing AI in QA?

Best practices include starting with a clear strategy, ensuring data quality, and involving human testers in the loop.