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eISSN: 2581-9615 || CODEN (USA): WJARAI || Impact Factor: 8.2 || ISSN Approved Journal

AI-Driven DevOps Acceleration: Orchestrating CI/CD Pipelines with Generative Models

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Amar Gurajapu *

AT and T, Network Systems, New Jersey, United States.

Review Article

World Journal of Advanced Research and Reviews, 2026, 29(01), 1033-1038

Article DOI: 10.30574/wjarr.2026.29.1.0154

DOI url: https://doi.org/10.30574/wjarr.2026.29.1.0154

Received on 29 November 2025; revised on 15 January 2026; accepted on 19 January 2026

Modern software delivery pipelines face growing complexity and manual overhead when authoring code scaffolds, tests, and infrastructure configurations. We introduce “DevGen” an integrated Generative-AI(GenAI) assistant that embeds large language models at four pivotal stages of a CI/CD workflow, which includes feature breakdown, code templating, automated test synthesis, and pipeline authoring. By integrating DevGen into a GitHub Actions environment, we compare its performance against a traditional toolchain over multiple sprints. Our empirical analysis demonstrates a 30 % reduction in story completion lead time, a 25 % increase in automated test coverage, and measurable improvements in developer satisfaction. We conclude with recommendations for domain-specialized fine-tuning, closed-loop feedback, and security policy generation to further enhance AI-driven delivery.

DevOps; CI/CD; Generative AI; LLM Integration; Pipeline Automation; Developer Productivity

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2026-0154.pdf

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Amar Gurajapu. AI-Driven DevOps Acceleration: Orchestrating CI/CD Pipelines with Generative Models. World Journal of Advanced Research and Reviews, 2026, 29(01), 1033-1038. Article DOI: https://doi.org/10.30574/wjarr.2026.29.1.0154.

Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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