Intelligent DevOps : Leveraging AI to Revolutionize Software Delivery
DOI:
https://doi.org/10.32628/CSEIT241061165Keywords:
AI-DevOps Integration, Intelligent Software Delivery, Predictive Release Management Automated Code Analysis, Machine Learning Pipeline OptimizationAbstract
This article examines the transformative impact of artificial intelligence on DevOps practices and software delivery methodologies, presenting a comprehensive analysis of current implementations, challenges, and future directions. The article explores how AI-driven solutions are revolutionizing traditional DevOps workflows through advanced automation, predictive analytics, and intelligent decision-making systems. Key focus areas include the optimization of CI/CD pipelines through pattern recognition and machine learning algorithms, the enhancement of code quality through automated review systems, and the implementation of predictive analytics for proactive risk management in release cycles. The article delves into practical applications of AI in testing automation, user behavior simulation, and anomaly detection while addressing critical considerations such as model drift management and integration complexities. Through article analysis of industry implementations and emerging trends, this article demonstrates how organizations can achieve significant improvements in deployment efficiency, code quality, and operational reliability through AI-DevOps integration. The article indicates that organizations implementing AI-driven DevOps practices have experienced substantial reductions in deployment failures, accelerated release cycles, and enhanced software quality metrics. This article provides valuable insights for practitioners and researchers seeking to understand and implement AI-powered DevOps solutions while navigating the associated technical and organizational challenges.
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Forsgren, N., Humble, J., & Kim, G. (2023). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press. URL: https://itrevolution.com/product/accelerate/
Tatineni, Sumanth. (2023). “AIOps in Cloud-native DevOps: IT Operations Management with Artificial Intelligence”. Journal of Artificial Intelligence & Cloud Computing. 1-7. 10.47363/JAICC/2023(2)154. [Online] Available: http://dx.doi.org/10.47363/JAICC/2023(2)154
Vemuri, Naveen & Thaneeru, Naresh & Tatikonda, Venkata. (2024). “AI-Optimized DevOps for Streamlined Cloud CI/CD”. International Journal of Innovative Science and Research Technology. 9. 7. 10.5281/zenodo.10673085. https://zenodo.org/records/10673085
Yonatha Almeida, Danyllo Albuquerque, Emanuel Dantas Filho, et al., “AICodeReview: Advancing code quality with AI-enhanced reviews”,SoftwareX, Volume 26, 2024, 101677, ISSN 2352-7110, https://www.sciencedirect.com/science/article/pii/S2352711024000487
Praveen Kumar Mannam, DevOps, “How New Tech Elevates Release Management’s Quality Standards”. [Online] Available: https://devops.com/how-new-tech-elevates-release-managements-quality-standards/
Nicola Paltrinieri, Louise Comfort, Genserik Reniers, “Learning about risk: Machine learning for risk assessment” ,Safety Science, Volume 118, 2019, Pages 475-486, ISSN 0925-7535, https://doi.org/10.1016/j.ssci.2019.06.001
Sudharsanam, Sharmila Ramasundaram, Praveen Sivathapandi, and Deepak Venkatachalam. "Enhancing Reliability and Scalability of Microservices through AI/ML-Driven Automated Testing Methodologies." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 480-514. https://aimlstudies.co.uk/index.php/jaira/article/view/195
NimbleBox.ai. “Model Drift in Machine Learning: How to Detect and Avoid It” [Online] Available: https://blog.nimblebox.ai/machine-learning-model-drift
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88. [Online] Available: https://aimlstudies.co.uk/index.php/jaira/article/view/103
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