CRAF-CT - Cloud Risk-Aware Adaptive Framework for Cloud Testing

Abstract

Cloud computing has become the backbone of modern software deployment due to its scalability, flexibility, and cost-effectiveness. As organizations increasingly migrate mission-critical applications to cloud environments, ensuring software quality through effective testing has become a significant challenge. Conventional cloud testing approaches generally execute predefined test suites without considering the continuously changing operational risks associated with cloud infrastructures. Consequently, valuable testing resources may be spent on low-risk components while high-risk services remain insufficiently tested. This paper proposes CRAF-CT (Cloud Risk-Aware Adaptive Framework for Cloud Testing), a novel framework that integrates cloud infrastructure monitoring, application log analysis, risk assessment, and adaptive test prioritization into a unified testing process. The framework continuously collects operational data from cloud resources, including application logs, system logs, API responses, resource utilization metrics, and security events. A dynamic risk assessment model evaluates the likelihood and impact of potential failures for each cloud service. Based on the calculated risk score, the framework automatically prioritizes and selects test cases, enabling testing efforts to focus on the most critical components. The proposed framework aims to improve defect detection efficiency, reduce unnecessary regression testing, minimize execution time, and enhance software reliability in dynamic cloud environments. A prototype implementation and experimental evaluation are planned to compare CRAF-CT with conventional cloud testing techniques using performance metrics such as defect detection rate, testing time, resource utilization, and risk coverage. The expected outcome demonstrates that adaptive risk-aware testing provides a more efficient and intelligent approach for cloud software quality assurance.