Abstract
ingInfrastructure-as-Code (IaC) is a systems management practice that involves managing and provisioning computing infrastructure through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. IaC is an essential contribution to the complete automation of the entire software lifecycle in a Development and Operations (DevOps) context. The deployment and management of software through coded configuration, monitoring, and analysis is the IaC solution. In recent times, artificial intelligence (AI)—including generative AI, machine learning, and related techniques—offers opportunities to improve techniques across the IaC life cycle from IaC code generation to its deployment and runtime analysis. We conducted a comprehensive and systematic literature review for all IaC code development and operations phases, considering IaC as a specific software type that we map to the DevOps model. We present the bibliographic review results and investigate in which phases and how AI can enhance IaC techniques by extracting a framework of phase-specific AI contributions and research challenges, contrasting, in particular, generative AI and machine-learning applications across the phases. Key findings include Large Language Models (LLMs) dominating generation and Machine Learning (ML) dominating analysis activities, also showing that operations phases are less studied than IaC development. This review extends previous literature reviews by covering the full DevOps lifecycle, developing a phase-specific taxonomy of AI techniques for IaC, and aligning a comprehensive analysis of research challenges and directions with those that benefit developers by highlighting current innovations and pointing researchers to future directions.