Abstract
This disclosure presents an AI-powered solution that leverages Large Language Models (LLMs) and machine learning techniques to identify, analyze, and consolidate redundant test cases. In enterprise projects that are maintained for long period of time, test repositories accumulate significant duplication and redundancy, due to requirement changes in the project, which create significant operational challenges. The approach streamlines test repositories, optimizes tests, and reduces execution overhead. This methodology demonstrates improvements in long-running system tests where traditional manual optimization approaches become impractical due to scale and complexity.
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Recommended Citation
INC, HP, "AI-Powered Framework for test redundancy detection and optimization in Software Projects", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9201