发表机构
University of Michigan; The University of Hong Kong; University of California, Berkeley（密歇根大学; 香港大学; 加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对DevOps程序测试中云模拟器构建难的问题，提出基于神经符号代码合成的CloudEmu方法，其在AWS、GCP服务上的覆盖率与准确性优于手动开发的LocalStack。
AI 中文摘要
DevOps编程（例如使用CLI/API脚本或IaC框架）是云基础设施管理的关键。与传统编程任务不同，DevOps程序测试需要针对实际云资源进行配置和执行，这通常耗时、不安全且成本高昂。云模拟器因简化DevOps程序测试而受到欢迎，它们一般是API级别的模拟程序，可在本地环境中执行DevOps程序。不过，构建这些模拟器仍具挑战性：开发者必须手动解读大量云文档，并为每个服务、API及其交互手工编写逻辑，这无法扩展到云的复杂性，且随着服务和API的演变，云还是一个不断变化的目标。CloudEmu是一种基于云文档通过神经符号代码合成构建模拟器的自动化方法，其核心思路是结合大语言模型（LLMs）在文档理解和代码生成方面的通用优势，以及针对云的符号抽象（可抑制幻觉并大规模保证精度），同时将真实云作为神谕用于自动化测试、修复和对齐。我们的评估显示，CloudEmu在主要云服务商（AWS和GCP）的服务上，在覆盖率和准确性方面均有效，且它优于现有领先工具LocalStack，后者是由大型工程师团队耗时十年手动开发的。
英文摘要
DevOps programming (e.g., using CLI/API scripts or IaC frameworks) is key to cloud infrastructure management. Unlike traditional programming tasks, DevOps program testing needs provisioning and execution against actual cloud resources, which is often time-consuming, unsafe, and costly. Cloud emulators have gained popularity for easing DevOps program testing; they are generally API-level mocks that can execute DevOps programs in a local environment. Still, building these emulators remains challenging: developers must manually interpret extensive cloud documentation and handcraft logic for each service, API, and their interaction. This does not scale to the complexity of the cloud, which is further a moving target as the services and APIs evolve. CloudEmu is an automated approach that constructs emulators based on cloud documentation via neurosymbolic code synthesis. The key idea is to combine LLMs' general strengths in documentation understanding and code generation with cloud-specific symbolic abstractions that suppress hallucinations and enforce precision at scale, while using the real cloud as an oracle for automated testing, repair, and alignment. Our evaluation shows the effectiveness of CloudEmu on major cloud provider (AWS and GCP) services in both coverage and accuracy. CloudEmu outperforms the existing leading tool LocalStack, which was manually developed by a large team of engineers over a decade.
Comments12 pages, 8 figures, 5 tables, Under review
