AI-Assisted Kubernetes Regression Automation for Secure SAP Cloud Software Delivery and Enterprise Management
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Abstract
The increasing adoption of cloud-native architectures has transformed SAP software delivery, creating a need for secure, scalable, and highly automated regression testing. Kubernetes provides a flexible orchestration platform for deploying SAP-related cloud services, microservices, APIs, databases, and supporting enterprise applications, but its dynamic nature also introduces testing challenges involving configuration drift, container dependencies, deployment variability, security vulnerabilities, and rapidly changing infrastructure. This study proposes an AI-assisted Kubernetes regression automation framework for secure SAP cloud software delivery and enterprise management. The proposed approach integrates artificial intelligence with continuous integration and continuous delivery pipelines to identify high-risk software changes, automatically prioritize regression tests, detect anomalous deployment behavior, validate Kubernetes configurations, and strengthen security controls. The methodology combines historical test results, application telemetry, Kubernetes events, configuration information, vulnerability findings, and deployment metadata to develop intelligent test-selection and risk-prediction mechanisms. Automated regression execution is integrated with container security, policy validation, observability, and rollback mechanisms to support reliable enterprise software delivery. The study evaluates the framework using measures including regression-test effectiveness, defect detection rate, execution time, resource utilization, false-positive rate, security issue detection, and deployment reliability. The proposed framework aims to reduce redundant testing while preserving critical coverage, improving security, accelerating SAP cloud releases, and providing enterprise managers with data-driven visibility into software quality and operational risk
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