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Development of a Model-Driven DevOps Solution Based on Context-Engineered LLM Code Generation: PROFES Doctoral Symposium
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AI
DOI：10.1007/978-3-032-12092-2_10.png)
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Abstract
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DevOps practices have been widely studied since 2009, nonetheless automated generation of Continuous Integration and Continuous Delivery (CI/CD) pipelines from high-level software architecture models remain underexplored. This paper addresses that gap through Model-Driven DevOps with AI (MDDOAI), a model-to-code approach that automates pipeline synthesis from architectural intent and enriches the output with context engineering method. The solution combines ATL based model transformations with Acceleo-driven code generation to produce deployable CI/CD configurations. For Quality Evaluation the approach includes runtime as validation and unsupervised code regeneration to ensure LLM produced pipelines meet functional requirements. A working prototype demonstrates the feasibility of scalable, model-driven pipeline automation, improving maintainability in modern DevOps environments.
Keywords:
DevOps cdot CI/CD automation
Model-Driven Engineering
Model transformation
Pipeline generation
Large Language Models
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