Ai-powered Test Automation: Revolutionizing Cloud Testing With Llms
Year: 2024
Type: article
Abstract: This article presents a novel testing framework that integrates AI and Large Language Models (LLMs) with serverless architectures for cloud-native application testing. The framework demonstrates significant improvements in testing efficiency, with a 78% reduction in manual test writing time and 91% accuracy in edge case identification. Leveraging advanced prompt engineering and serverless execution, the system achieves 65% faster test execution than traditional infrastructure while reducing operational costs by 45-60%. Empirical results from over 1,400 professionals show that organizations implementing this approach are 2.4 times more likely to be elite performers in software delivery, with automated test coverage increasing by 42% year-over-year. The framework's architecture combines LLM integration, prompt engineering, serverless orchestration, and feedback analysis systems, achieving 99.82% service reliability and processing capabilities of 32,000 requests per minute.
Author: Researcher
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911979836
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