METHOD FOR ASSESSING THE QUALITY OF 5G CORE NETWORK SOFTWARE

Abstract

The fifth-generation mobile networks (5G) have introduced fundamental changes in how telecommunication systems are designed and operated. Among these, the 5G Core (5GC) plays a central role by managing control and user plane functions, mobility, service-based interactions, and policy enforcement. As the complexity and modularity of 5GC grow — especially in the context of cloud-native architectures and continuous software delivery — the need arises for advanced methods to assess software quality effectively and reliably.

This paper presents a novel method for evaluating the quality of 5G Core software. The approach combines conventional quality metrics based on the ISO/IEC 25010 standard with additional indicators specifically relevant to 5G systems, such as latency, scalability, energy efficiency, fault tolerance, and adaptability. A three-tier model of quality assessment is developed, encompassing service-level, functional-level, and infrastructure-level metrics. The model computes an integral quality index (Q_total) that reflects the multi-dimensional performance and operational characteristics of the 5G Core software under varying conditions.

The proposed methodology was validated through simulation scenarios, including dynamic traffic loads, component upgrades, and node failures. Graphical analysis demonstrates how Q_total varies in response to system changes, and comparative experiments show improved precision and resilience of the proposed method over traditional approaches. The results indicate strong potential for integration into CI/CD pipelines and operator-grade monitoring environments.

The main contributions of this study include: a structured, customizable framework for 5GC software quality assessment; the incorporation of 5G-specific performance metrics into a standardized quality model; experimental evaluation demonstrating the method’s effectiveness and applicability in real-world scenarios.

The method offers practical value for telecom operators, software vendors, and integrators aiming to ensure reliability and service-level compliance in rapidly evolving 5G networks. Future research will focus on expanding the model for upcoming 6G systems, integrating machine learning for adaptive metric weighting, and deploying the method in operational network environments.