Survey of Predict DevOps Readiness based on Machine Learning

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has given software more reliability, and enable to scalable within a little period. Software development is became is more effective in world today in become a core competency for every business also the machine learning is as obtained interest. As companies increasingly use DevOps practices to enhance efficiency, quality, and timely software delivery, predicting DevOps readiness becomes increasingly vital and important to ensure successful software implementation. the contribution of this paper presents a critical framework for machine learning and its use in predicting DevOps readiness in organizations through team collaboration and workflow monitoring using machine learning models and algorithms that are used for optimization, accuracy, and effective delivery of good results.

Authors

Publication Details

Journal
International Research Journal of Innovations in Engineering and Technology
Published
2025-01-01
DOI
https://doi.org/10.47001/irjiet/2025.905013
Citations
1
Primary Topic
Technology Assessment and Management
Type
article
Field-Weighted Citation Impact
1.30
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article

Survey of Predict DevOps Readiness based on Machine Learning

Ibrahim Ahmed Saleh, Zahraa Nuri Hasan
1 citations
International Research Journal of Innovations in Engineering and Technology
Technology Assessment and Management
1.30
article

Survey of Predict DevOps Readiness based on Machine Learning

Ibrahim Ahmed Saleh, Zahraa Nuri Hasan
article en
1 citations

Abstract

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has given software more reliability, and enable to scalable within a little period. Software development is became is more effective in world today in become a core competency for every business also the machine learning is as obtained interest. As companies increasingly use DevOps practices to enhance efficiency, quality, and timely software delivery, predicting DevOps readiness becomes increasingly vital and important to ensure successful software implementation. the contribution of this paper presents a critical framework for machine learning and its use in predicting DevOps readiness in organizations through team collaboration and workflow monitoring using machine learning models and algorithms that are used for optimization, accuracy, and effective delivery of good results.

International Research Journal of Innovations in Engineering and TechnologyVol. 09(05)
Openalex Percentile: Top 21%
Technology Assessment and Management
1.30
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