AutoPipelineAI: Context-Aware CI/CD Pipeline Generation from Natural Language

2026-06-04

AI Summary

This work addresses the challenge developers face in efficiently authoring CI/CD configurations due to limited DevOps expertise by proposing a large language model (LLM)-based, context-aware generation approach. The method leverages both natural language descriptions and repository structure to automatically produce accurate and executable pipeline configurations for platforms such as GitHub Actions and GitLab CI/CD. Integrated with automated validation and human-in-the-loop feedback mechanisms, this framework is the first to combine repository context understanding with natural language-driven configuration synthesis. Experimental results demonstrate that the approach significantly lowers the barrier to DevOps adoption, markedly improves the accuracy and validity of generated configurations, and substantially reduces manual configuration effort.

Abstract

Modern software development relies on CI/CD pipelines to automate testing, building, and deployment operations. Configuring DevOps pipelines is challenging and time-consuming, as developers must understand platform-specific syntax and manually create configuration files. This complexity can lead to configuration errors and reduced productivity, especially for developers with limited DevOps experience. This paper introduces the AutoPipelineAI system, which generates CI/CD pipeline configurations using natural language descriptions. The proposed solution uses large language models (LLMs) to translate developer intent, analyze repository structures, and create specific pipeline scripts for environments like GitHub Actions and GitLab CI/CD. It integrates repository-aware analysis, automated validation systems, and a feedback mechanism that confirms the accuracy and usability of the created pipelines. We present the system architecture, its implementation, and an assessment framework designed to measure generation precision, configuration validity, and reduction in setup effort compared to manual pipeline creation. AutoPipelineAI illustrates how LLMs can simplify the complexity of DevOps configuration and enhance developer access to continuous delivery methods. Evaluation results provide early evidence that repository-aware, natural-language-driven CI/CD generation is a viable and promising paradigm for reducing the complexity of DevOps configuration and enabling more accessible software delivery automation.

Problem

• CI/CD pipeline configuration
• DevOps complexity
• natural language interface
• developer productivity
• configuration errors

Innovation

• AutoPipelineAI
• CI/CD pipeline generation
• large language models
• context-aware DevOps
• natural language to configuration