International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing

Author(s) Saili Krishna Maliye
Country United States
Abstract This article proposes a conceptual framework for AI-powered testing of financial cloud applications by integrating Large Language Models (LLMs) with serverless computing architecture. The framework addresses critical challenges in financial software testing, including regulatory compliance, security validation, and performance scalability. Through theoretical analysis, the design incorporates
sophisticated prompt engineering techniques and automated compliance monitoring systems to achieve high compliance validation accuracy while potentially reducing monitoring costs. The architecture suggests superior performance capabilities, potentially processing concurrent test executions efficiently while reducing operational costs compared to traditional testing infrastructures. The framework design anticipates improvements in test generation efficiency, regulatory compliance coverage, and overall testing costs while maintaining robust security standards through enhanced vulnerability detection mechanisms. The theoretical integration of LLMs with serverless computing shows promise in addressing the complex challenges of financial software testing, offering a potential pathway to a scalable, cost-effective solution that could meet the stringent requirements of modern financial cloud applications while ensuring regulatory compliance and security.
Keywords AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-13
Cite This AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing - Saili Krishna Maliye - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30383
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30383
Short DOI https://doi.org/g8rd4x
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