aspg-18-4079-1792025500 20261015005140 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 2 Shankar Ramakrishnan E. K. 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mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Recently, Deep learning (DL) models are increasingly used in Test Case Prioritization (TCP) tasks combining partial and imperfect test case (TC) information into accurate prediction models. Various DL algorithms have been created to improve TC failure prediction and prioritization in CI settings. Among them, Deep Reinforcement Prioritizer (DeepRP) model is developed using Deep Reinforcement Learning (DRL) and Deep Neural Network (DNN) for efficient TCP on huge test suites. But, the model's labelling task is interrupted early, creating difficulty in learning TC features for unlabeled training TCs due to limited resources. To solve this, Deep Graph Reinforcement Prioritizer (DeepGRP) is proposed in this paper to learn the TC features from unlabeled training data for efficient TCP in Regression Testing (RT). In this method, graph neuron stimulation attributes for TCs are created to retrieve the activation graph across DNN layers of DeepRP. The connectivity neuron link defines the activation graph. The proposed deep graph (DG) recognizes the DNN neurons as nodes and the adjacency matrix as the connectivity link among the nodes. Also, the message passing mechanism is applied to aggregate the structural information from the adjacency matrix with neighbouring node features to enhance TCP. By applying this mechanism, DeepGRP captures the high-order dependencies among neurons for efficient activation features which overcomes the traditional activation models and improves the TCP at large scale RT. The DG model prioritizes TCs using Learning-to-Rank (L2R) which learns node attributes from TCs. This enables for better DNN testing efficiency by detecting vulnerabilities early and lower development time for efficient TCP and tackling the difficulty of learning TC characteristics for efficient TCP. Finally, the testing findings suggest that the DeepRP can improve the TCP for large TSs when compared to other common algorithms. 2026 361 374 4079 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180225 https://americaspg.com/journal/18/article/4079 https://americaspg.com/storage/81757258772.pdf