Analyzing the Relationship Between Cloud-Native Architectural Patterns and Software Quality in AI-Assisted Development

Abstract

Modern software systems are increasingly built as cloud-native, distributed applications, and an increasing share of their source code is now produced with the assistance of large language model (LLM) based coding tools. Both shifts change the way architectural decisions propagate into software quality. To investigate this relationship, we conducted a mixed-methods study combining controlled experimentation, empirical analysis, case studies and architecture visualization. We analyzed a sample of twenty-four software systems drawn from several domains, covering layered monolithic, modular monolithic, microservice and serverless event-driven architectures. In the experimentation phase we re-implemented a common reference application under four architectural designs and measured the quality of the resulting systems using ISO/IEC 25010 aligned metrics for maintainability, reliability and performance efficiency, together with delivery performance indicators. The empirical phase collected static-analysis metrics, defect records and deployment telemetry from the sampled systems and analyzed the relationship between architecture and quality using correlation and regression techniques. Case studies were used to understand how architectural decisions behave in specific real-world contexts, including one system in which a significant proportion of new code was generated by an AI coding assistant. Finally, architecture visualization and distributed tracing were used to expose dependency structures, architectural drift and latent hot spots. Our findings suggest that architecture remains the dominant controllable factor in software quality: designs that enforce explicit module boundaries and low coupling achieved higher maintainability and reliability irrespective of whether the system was deployed as a monolith or as services. We also found that AI-assisted development accelerates local code production but increases architectural conformance violations when boundaries are not encoded in tooling. The study contributes evidence- based guidance for architects, developers and engineering managers working on cloud-native, AI-assisted software systems.