AI Large Language Model-Based Framework for Intelligent Software Test Automation

Software testing remains one of the most labor-intensive stages of the software development lifecycle, and conventional automation frameworks rely on brittle, manually written scripts that require constant upkeep as applications evolve. The recent emergence of large language models (LLMs) capable of understanding natural-language requirements and producing syntactically correct code has opened a new path toward intelligent test automation. This paper synthesizes current research and proposes a layered architecture, referred to as the LLM-Driven Test Automation Framework (LTAF), that integrates retrieval-augmented context ingestion, LLM-based test-case generation, automated script synthesis for common automation stacks, a self-healing locator component, and a closed feedback loop between test execution and the generative model. We describe the architecture and design rationale of each component, discuss how the framework could be evaluated against established baselines such as search-based test generation, and summarize the reported strengths and limitations of LLM-based testing approaches drawn from the current body of work. We further identify open challenges, including the test-oracle problem, hallucinated assertions, flaky-test diagnosis, and data-privacy concerns when proprietary code is shared with external model providers, and we outline directions for future research toward reliable, self-repairing test automation pipelines.