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Ali, Salmaan (2024) Navigating AI Integration - From Theory to Practice: A Scoping Review of AI Integration Frameworks for DevOps. Masters thesis, Dublin, National College of Ireland.
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Abstract
The integration of Artificial Intelligence (AI) into DevOps workflows has dawned a paradigm shift that promises improvements across all stages of a Continuous Integration/ Continuous Deployment (CI/CD) routine. Despite the potential benefits, this unification faces challenges like integration complexity, data privacy concerns, ensuring the reliability of AI-generated outputs, bias mitigation, and the need for new skill sets. This scoping review examines the current state of AI integration in DevOps by synthesising findings from, after applying inclusion and exclusion criteria, 28 recent research articles, offering a panoramic view of AI-enhanced DevOps. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMAScR) guidelines, this article analyses how AI techniques such as Large Language Models, Machine Learning and, Natural Language Processing are transforming various DevOps stages such as development, testing, security, and monitoring. This analysis reveals that AI integration can significantly boost productivity, with some studies reporting up to 65% more requirements implemented and a 70% reduction in development time. Additionally, this study provides a practical implementation guide and a phased roadmap for organisations looking to harness AI in their DevOps workflows. This scoping review aims to bridge the gap between academic research and industry practice by identifying critical knowledge gaps and proposing future research directions to fully unlock the potential of AI in DevOps.
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