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.

翻译：现代软件开发依赖于CI/CD流水线来自动化测试、构建和部署操作。配置DevOps流水线既具挑战性又耗时，因为开发人员必须理解平台特定语法并手动创建配置文件。这种复杂性可能导致配置错误和生产力下降，尤其对于DevOps经验有限的开发人员而言。本文介绍AutoPipelineAI系统，该系统利用自然语言描述生成CI/CD流水线配置。所提方案使用大型语言模型(LLMs)来翻译开发人员意图、分析仓库结构，并为GitHub Actions和GitLab CI/CD等环境创建特定流水线脚本。该系统集成了仓库感知分析、自动化验证系统以及用于确认所创建流水线准确性和可用性的反馈机制。我们展示了系统架构、实现方案以及评估框架，该框架旨在衡量生成精度、配置有效性以及与手动创建流水线相比的设置工作量减少程度。AutoPipelineAI展示了LLMs如何简化DevOps配置的复杂性，并增强开发人员对持续交付方法的可访问性。评估结果提供了初步证据，表明仓库感知、自然语言驱动的CI/CD流水线生成是一种可行且有前景的范式，可降低DevOps配置复杂性并实现更易用的软件交付自动化。