Automated Synthesis of Cloud Emulators
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Abstract (arXiv)
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.
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References · 13
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 1,002
- 2021Jigsaw: Large Language Models meet Program Synthesiscited by 103
- 2023nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Modelscited by 60
- 2023Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?cited by 57
- 2017Learn&Fuzz: Machine Learning for Input Fuzzingcited by 23
- 2022Automated Test Generation for REST APIs: No Time to Rest Yetcited by 13
- 2023Large Language Models Based Automatic Synthesis of Software Specificationscited by 13
- 2025Cloud Infrastructure Management in the Age of AI Agentscited by 9
- 2022Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documentscited by 8
- 2025Automated Cloud Infrastructure-as-Code Reconciliation with AI Agentscited by 5
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