One of the most practical applications of AI for coding is in test automation. Writing and maintaining test scripts can be time-consuming, especially when applications change frequently. This is where AI-powered tools are starting to make a real difference.

AI can help generate unit, integration, and end-to-end tests automatically by analyzing codebases or even live traffic. It can also detect flaky tests, suggest assertions, and adapt test cases when APIs or UI elements change. This reduces the effort spent on script maintenance and helps QA teams keep up with fast-paced release cycles.

For developers, this means less manual boilerplate and fewer repetitive assertions, while testers gain higher coverage with reduced effort. The combination of AI-driven test generation and human review creates a more reliable and efficient automation pipeline.

As teams continue to adopt CI/CD, AI-assisted testing and use of ai for coding are becoming a key enabler for faster, more stable releases.