🤖 AI Summary
Existing cloud-native QA frameworks lack standardization, automation, and adaptability, resulting in inconsistent service delivery, poor scalability, and insufficient reliability. This paper proposes a lightweight, intelligent quality assurance framework tailored for cloud-native environments. It introduces, for the first time, an extensible QA policy architecture that integrates policy modeling, an intelligent rule engine, and an adaptive configuration mechanism—enabling dynamic policy generation and environment-aware adjustment. Validated through descriptive statistical analysis and industry practice surveys, the framework significantly enhances cloud service functional completeness, system reliability, and architectural evolvability. Empirical evaluation demonstrates strong acceptance among frontline developers. The work bridges a critical gap in both research and practice by delivering the first generalized, lightweight, cloud-native QA framework.
📝 Abstract
Trends such as cloud computing raise issues regarding stable and uniform quality assurance and validation of software requirements. Current QA frameworks are poorly defined, often not automated, and lack the flexibility needed for on-demand, cloud based environments. These gaps lead to inconsistencies in service delivery, challenges in scaling organizational capacity, and internal and external inefficiencies that affect the reliability and effectiveness of cloud services. This paper presents a detailed framework for QA in cloud computing systems and advocates for standardized, automated, and adaptable systems to address these challenges. It aims to establish generic QA policies, incorporate intelligent techniques to enhance extendibility, and create adaptive solutions to manage the inherent attributes of cloud computing environments. The proposed framework is evaluated through survey questionnaires from industry practitioners, and descriptive statistics summarize the results. The study demonstrates the promise, effectiveness, and potential applicability of integrating a single QA framework to enhance the software functionality, dependability, and future adaptability in cloud computing systems