Evaluating Cloud-Native ERP Architectures Using AI-Based Software Engineering Metrics and DevOps Automation
DOI:
https://doi.org/10.15662/IJARCST.2024.0706006Keywords:
Cloud-native ERP, AI-based metrics, DevOps automation, DORA metrics, predictive analytics, anomaly detection, automated remediation, enterprise architecture, software engineering metrics, continuous integration, continuous delivery, system resilience, operational efficiency, modernization, case studiesAbstract
The integration of Artificial Intelligence (AI) into cloud-native Enterprise Resource Planning (ERP) systems is transforming traditional enterprise architectures by enhancing scalability, agility, and operational efficiency. This paper presents a comprehensive evaluation framework that leverages AI-driven software engineering metrics and DevOps automation to assess the performance and effectiveness of cloud-native ERP architectures. The proposed framework encompasses key performance indicators (KPIs) such as deployment frequency, lead time for changes, change failure rate, and mean time to recovery (MTTR), aligning with the DORA metrics for DevOps performance. Additionally, the framework incorporates AI-based tools for predictive analytics, anomaly detection, and automated remediation, facilitating proactive management of ERP systems. Through case studies and empirical analysis, the paper demonstrates the practical application of the framework in real-world scenarios, highlighting its impact on operational efficiency and system resilience. The findings underscore the significance of integrating AI and DevOps practices in the modernization of ERP systems, offering valuable insights for organizations aiming to enhance their enterprise architecture in the cloud-native era.
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