The Role of Smart Cloud Systems in Quality Assurance: A Review on Artificial Intelligence (AI) and Machine Learning Techniques (ML)

Abstract

Quality assurance (QA) is becoming more difficult as cloud computing grows into complex and dynamic systems. Smart cloud environments frequently require traditional QA approaches that are not sufficient to cover the real-time responsiveness issues, scalability expectations, and automation requirements. The present paper explores a revolutionary potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing QA frameworks towards cloud systems. It looks into the way some of the AI-based mechanisms like deep learning, natural language processing (NLP), and anomaly detection automate the creation of tests, enhance fault detection, and facilitate the incident management proactively. The ML would be helpful in reinforcing adaptive testing, predictive analytics, and smart resource allocation, amongst other factors, making the test much more specific, quick, and reliable. The research has an in-depth discussion of AI/ML-driven QA tools and methods and illustrates their use in such disciplines as healthcare, software engineering, manufacturing, and education. The comparative literature review also shows that they can be effective in the improvement of performance, minimization of manual efforts and attainment of service-level objectives. However, the paper also identifies ongoing challenges related to model transparency, generalizability, and regulatory compliance, especially in safety-critical systems. The findings confirm that AI and ML are not just auxiliary tools but essential components for building robust, autonomous, and cost-effective QA systems in the cloud. The paper concludes with suggestions for future research aimed at developing standardized, explainable, and regulation-compliant QA frameworks suitable for heterogeneous and rapidly evolving smart cloud ecosystem.