AI 中文总结
研究探讨前沿大语言模型能否利用存储库内容为多服务应用生成配置，通过对三个异构存储库评估发现生成环境虽能运行，但有遗漏，进而区分功能正确性与部署意图保真度并得出最小显式部署规范。
AI 中文摘要
大语言模型（LLMs）越来越多地用于从存储库工件生成可执行软件环境。但功能可执行性不一定意味着符合架构、安全、工作流程和生产意图。本研究调查前沿LLM能否在不访问开发者编写的部署工件的情况下，使用存储库内容为多服务应用生成Dockerfile和Docker Compose配置。使用确定性端到端HTTP预言机和手动结构比较对三个异构存储库进行评估。所有三个生成的环境都能正常运行，但有一个需要将Rust基础镜像从1.85版更新到1.88版。模型正确重建了服务拓扑、应用端口等，但始终遗漏网络分段等内容。基于这些观察，研究正式区分了功能正确性和部署意图保真度，并分析得出了无法从存储库工件可靠推断的信息的最小显式部署规范。
英文摘要
Large Language Models (LLMs) are increasingly used to generate executable software environments from repository artifacts. However, functional executability does not necessarily imply conformity with architectural, security, workflow, and production intent. This study investigates whether a frontier LLM can generate Dockerfiles and Docker Compose configurations for multi-service applications using repository contents without access to developer-authored deployment artifacts. Three heterogeneous repositories combining Python, Node.js, .NET, React, Rust, Java, Redis, PostgreSQL, and MySQL-compatible infrastructure were evaluated using deterministic end-to-end HTTP oracles and manual structural comparison. All three generated environments became functionally operational, although one required a Rust base-image update from version 1.85 to 1.88. The model correctly reconstructed service topology, application ports, infrastructure dependencies, service hostnames, a background worker, hidden proxy configurations, and a file-based Docker secrets mechanism. However, it consistently omitted network segmentation, multi-stage builds, dependency-layer caching, live-reload volumes, production frontend serving, restrictive backend-port policies, and cross-platform build logic. Based on these observations, the study formalizes the distinction between functional correctness and deployment-intent fidelity and analytically derives a minimal explicit deployment specification for information that cannot be reliably inferred from repository artifacts.
Comments25 pages, 8 figures, 15 tables
