Integrating Large Language Models for Automated Test Case Generation in Complex

Author(s)	Hariprasad Sivaraman
Country	USA
Abstract	When software systems are larger, complex and mature, the quality assurance process gets difficult. Test case generation in a conventional manner, often manually, is a time and resource-intensive process that fails to cope with the fast cycles that modern dynamic development environments push. In this paper a novel technique to automatically generate test cases based on the new state-of-the-art Large Language Models (LLMs) is being proposed. LLMs, an application of Natural Language Processing (NLP) techniques, provide immense benefits such as the ability to analyze unstructured data for e.g., system documentation, user stories, and historical test data to automatically generate test cases. Integrating LLMs into the test automation pipeline can result in increased test coverage, decreased manual overhead, and enhanced system stability to the organization. Conventional approaches are contrasted with LLM-centric methodologies, to provide a detailed sequential approach to integrating LLMs, and highlight challenges and limitations with respect to the practical adoption of LLMs for testing. This paper provides directions for future research in this fast-growing area.
Published In	Volume 2, Issue 1, January-February 2020
Published On	2020-02-26
DOI	https://doi.org/10.36948/ijfmr.2020.v02i01.20750
Short DOI	https://doi.org/g8xk9v

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International Journal For Multidisciplinary Research