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AI-Driven Continuous Verification Framework for Cloud-Native Order Management Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 98 |
| Year of Publication: 2026 |
| Authors: Satish Reddy Budati |
10.5120/ijcae0b30b596eb5
|
Satish Reddy Budati . AI-Driven Continuous Verification Framework for Cloud-Native Order Management Systems. International Journal of Computer Applications. 187, 98 ( Apr 2026), 12-16. DOI=10.5120/ijcae0b30b596eb5
Abstract
Cloud-native order management systems (OMS) built on microservice architectures must maintain high reliability while supporting frequent deployments and dynamic workloads. Conventional regression testing approaches often struggle to detect performance degradations in distributed cloud environments where services interact asynchronously. This paper presents an AI-driven continuous verification framework designed to monitor and validate the behavior of cloud-native OMS platforms during the deployment lifecycle. The framework integrates synthetic transaction data generation with machine-learning-based regression analytics to simulate realistic order processing scenarios while avoiding exposure of sensitive production data. A dataset consisting of 417 synthetic order transactions was generated to emulate a range of operational conditions including peak workloads, service delays, and edge-case failures. Telemetry metrics collected from distributed services such as latency, CPU utilization, and memory consumption were analyzed using regression-based anomaly detection models. Experimental observations indicate that the proposed framework can identify subtle performance regressions and service anomalies before they affect production environments. By integrating automated verification within CI/CD pipelines, the framework enables faster release cycles while maintaining operational reliability. The study demonstrates the practical value of combining synthetic data generation with intelligent analytics for scalable and privacy-preserving validation of enterprise cloud platforms.
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