International Journal of Innovative Research in Computer Science and Technology
Year: 2025, Volume: 13, Issue: 4
First page : ( 1) Last page : ( 14)
Online ISSN : 2350-0557.
Satyadhar Joshi
DOI: 10.55524/ijircst.2025.13.4.1 | DOI URL: https://doi.org/10.55524/ijircst.2025.13.4.1
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Satyadhar Joshi
This paper presents a comprehensive review of Generative AI applications in DevOps automation, covering 50 key research works published between 2023-2025. By synthesizing insights from recent research and industry practice, this paper identifies the top terms, theories, and algorithms shaping the field and offers a forward-looking perspective on the evolution of AI-driven DevOps through 2029. We analyze the transformative impact of AI-driven solutions across the software development lifecycle, including code generation, infrastructure management, continuous integration/delivery, and Kubernetes operations. The present paper is a thorough review of how generative AI and agentic workflows are changing the way modern software systems are developed, deployed, and operated. We look at the introduction of automation in continuous integration and continuous deployment (CI / CD) pipelines using AI / ML, the rise of cloud-native platforms (e.g. Docker and Kubernetes), and the Infrastructure as Code (IaC) and the rise of progressive delivery models. The paper points out the positives of these developments, which consist of efficiency, reliability, and speed of innovation, and also focuses on the issue of security, compliance, observability, and skill development. The review is a systematic study of how generative AI improves the efficiency of deployment, monitoring, and the general development workflow and solves the problem in cloud-native environments. In our analysis, we identified the rising trends in AI agents to use in DevOps, containerized AI applications, and large language models integrated into the existing DevOps toolchains. It is a review article and all the findings mentioned are by their respective authors.
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AVP, Risk and Analytics, Bank of America, Jersey City, United States
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