The software testing landscape is undergoing a paradigm shift with the advent of Software Engineering 3.0, where large language models (LLMs) are becoming central to quality assurance. This article provides a practical guide to implementing LLM-driven testing, covering automated test case generation from requirements, intelligent defect prediction using historical data, and dynamic test execution orchestration. Drawing from real-world experiences in the Chinese tech ecosystem, it highlights how teams can reduce manual effort by up to 40% while improving coverage. Key challenges include model hallucination, data privacy, and integration with existing CI/CD pipelines. For engineering leaders evaluating AI in QA, this offers a concrete starting point with both opportunities and pitfalls clearly outlined.
A trend report on how large language models are transforming software testing, with actionable insights from the Chinese tech ecosystem.