Intelligent Test Automation: A Multi-Agent LLM Framework for Dynamic Test Case Generation and Validation

Author(s)	Pragati Kumari
Country	India
Abstract	Automated software testing is essential in modern software development, ensuring stability and resilience. This study describes a unique technique for using the capabilities of Large Language Models (LLMs) via a system of autonomous agents. These agents collaborate to dynamically generate, validate, and execute test cases based on specified requirements [1, 2]. By iteratively improving test cases via agent-to-agent communication, the system improves accuracy and effectiveness. Our implementation, which uses AutoGen and Python's unittest framework, shows how this method helps to maintain excellent software quality. Experimental evaluations across a variety of test scenarios demonstrate the versatility and efficiency of our framework, Intelligent Test Automation (ITA), emphasizing its promise for increasing automated software testing [3, 4].
Keywords	Intelligent Test Automation (ITA), Large Language Models (LLMs), Multi-Agent Systems, Automated Software Testing, Test Case Generation
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-05
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2232
Short DOI	https://doi.org/g869w7

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International Journal on Science and Technology