Optimizing Software Deployment: A Framework for Automation through DevOps, CI/CD, and Containerization
DOI:
https://doi.org/10.55640/Keywords:
DevOps, Continuous Integration (CI), Continuous Deployment (CD)Abstract
Purpose: This paper proposes a conceptual framework to address the inefficiencies and inaccuracies inherent in manual software deployment processes within U.S. corporations. The primary objective is to demonstrate how the integration of DevOps culture, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and containerization technologies can create a robust system for software deployment automation.
Methodology: A systematic literature review of 20 peer-reviewed articles and industry reports was conducted. The study synthesizes key principles from DevOps, agile methodologies, and modern software engineering practices to construct a multi-faceted conceptual framework. The analysis focuses on identifying the synergies between cultural, methodological, and technical components that contribute to successful automation.
Findings: The proposed framework consists of four interconnected pillars: the DevOps philosophy as the cultural foundation, agile methodologies for iterative development, the CI/CD pipeline as the technical engine for automation, and containerization (specifically Docker) as the means to ensure environmental consistency. The findings indicate that the integrated adoption of these elements can significantly increase deployment speed, reduce error rates, and enhance the overall reliability of the software delivery lifecycle. The study also identifies key challenges, including cultural resistance and toolchain complexity, and offers corresponding mitigation strategies.
Originality/Value: This paper provides a novel, integrated framework that combines the often-siloed discussions of DevOps, CI/CD, and containerization. It offers a clear, actionable model for U.S. corporations seeking to transition from traditional, manual deployment methods to a modern, automated paradigm, thereby improving both efficiency and accuracy.
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Copyright (c) 2025 Diego Martínez, Nicolás Cabrera, Laura Benítez (Author)
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