Citation

Elijah, Graeson Joshua and M, Mythily and Farid, Fahmid Al and Addura G R, Neeraj and Silas, Salaja and Rajsingh, Elijah Blessing and Uddin, Jia and Abdul Karim, Hezerul (2026) AI-driven automation for CI/CD pipelines using attention-enabled reptile meta learning. Frontiers in Computer Science, 8. ISSN 2624-9898

Text AI-driven automation for CI_CD pipelines using attention-enabled reptile meta learning.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

Healthcare systems increasingly rely on software to support critical operations such as patient monitoring, diagnostic decision-making, and real-time data management, making reliability, speed, and continuous availability essential. The software development and deployment phases, therefore, play a crucial role in ensuring that the healthcare applications remain stable and compliant with regulatory standards. However, traditional Continuous Integration/Continuous Deployment (CI/CD) pipelines are largely reactive, relying on manual testing, delayed feedback, and post-failure recovery mechanisms, which are time-consuming and prone to errors. Such limitations lead to deployment instability, inefficient resource utilization, and increased operational risk in mission-critical healthcare environments. To overcome these challenges, automated and intelligent approaches are required to enable proactive failure detection, faster recovery, and reliable deployment. In this regard, a novel AI-driven self-adaptive framework, Continuous Integration/Continuous Deployment Attention-enabled Reptile Meta-Learning (CICD-ARML), is proposed. The proposed system continuously learns from pipeline telemetry and adapts to dynamic deployment conditions, reducing dependency on manual intervention. Experimental results demonstrate that CICD-ARML achieves a 96.8% build success rate, reduces mean recovery time by 35%, reduces deployment risk by 59%, and achieves 94% prediction accuracy. These findings highlight the effectiveness of the proposed framework in enhancing the reliabilit

Item Type:	Article
Uncontrolled Keywords:	Continuous deployment, healthcare software, optimization
Subjects:	R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 08:35
Last Modified:	02 Sep 2026 08:35
URII:	http://shdl.mmu.edu.my/id/eprint/16549

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