A Study on Test Coverage Mapping and Release Risk Prediction in Continuous Delivery of Cloud Services

Cloud service continuous delivery involves computing, storage, permission, scheduling, and monitoring modules. Because complex service dependencies may hide cross-service anomalies under insufficient test coverage, this study proposes a quality assessment method combining test coverage mapping and release risk prediction. A directed dependency graph is built for interfaces, resource creation, volume mounting, permission verification, read/write performance, exception recovery, cross-version compatibility, and rollback paths. GraphSAGE learns associations among service nodes, test cases, and historical failures, while CatBoost predicts release failure probability. The dataset contains 31 pipelines, 126 modules, 460 integration cases, 9 release-change types, 2,800 release records, and 72 million execution data points. The coverage graph identifies 37 high-risk uncovered nodes, with storage mounting, permission propagation, resource initialization, and cross-version compatibility accounting for 72.9%. CatBoost achieves 90.8% accuracy and 0.934 AUC. Fault injection shows that dependency timeouts, permission anomalies, and storage latency raise failure probability by 31.5%, 24.7%, and 19.2%. After adding critical-path tests, core coverage increases from 68.4% to 91.2%, and monthly rollbacks fall from 26 to 14. This method supports risk control for banking, insurance, medical, education, and SaaS cloud services.