Hu Zezhi, Marifel Grace Kummer
ABSTRACT
Enterprise-level server hardware testing faces significant challenges due to increasing hardware heterogeneity, high manual labor requirements, and limited intelligence in conventional testing frameworks. Traditional testing approaches often lack scalability, adaptability, and efficient defect detection, resulting in high operational costs and extended testing cycles. This study proposes an AI Agent Automated Testing System with Decision Support, a hardware automation testing platform developed on the Saturn framework to support intelligent configuration, execution, and monitoring of server hardware tests. The system integrates multi-agent collaboration, reinforcement learning, and deep learning techniques to automate the entire testing workflow. Its architecture consists of four core modules: adaptive testing strategy, intelligent data analysis, automated test execution, and a decision support component for hardware evaluation. The platform incorporates AI-driven configuration through natural language interaction, automated script generation, and real-time monitoring of distributed test tasks. Experimental evaluation demonstrates that the proposed system significantly improves testing performance, reducing the testing cycle by 63% and increasing hidden defect detection rates by 43% across 237 testing scenarios. Automated script generation reduced configuration time from approximately 3 hours to 82 seconds, while GPU resource utilization increased substantially during parallel testing. Furthermore, expert evaluation based on the ISO/IEC 25010 software quality model yielded an overall mean score of 4.52, indicating a very great extent of compliance with international software quality standards. The results confirm that the proposed system provides a scalable and intelligent solution for automated server hardware validation, enabling more efficient quality assurance and supporting data-driven decision-making in large-scale enterprise computing environments.
Keywords: AI Agent, Automated Testing System, Multi-Agent Collaboration, Decision Support System, Reinforcement Learning, Deep Learning, ISO 25010 Standards, Server Hardware Testing
https://doi.org/10.57180/jdup2938