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.
Abstract
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.
This study investigates the use of Large Language Models to detect security misconfigurations directly from cloud API response data and evaluates each model’s capability to accurately determine the number of misconfigurations and generate clear, actionable security explanations.
A. Krishna, Farzana Zahid· Pragmatic Cybersecurity· 0 citations
This paper describes a framework that brings together three capabilities: automatic generation of new test cases from API specifications and usage data, self-healing of broken test scripts through similarity-based and learned matching, and risk-based prioritisation of test execution using a reinforcement learning agent...
Ihor Diakonov· Journal of Artificial Intell...· 0 citations
Infrastructure as Code (IaC) enables the automated provisioning of cloud infrastructures, yet syntactically valid configurations may remain unsuitable for the applications they support. Addressing such issues requires knowledge of both the infrastructure and its runtime behavior. We introduce POLIAC, an LLM-based frame...
Haitam El Hayani, Jolan Philippe, Stéphanie Challita· Proceedings of the 3rd Inter...· 0 citations
E2E-SWE is introduced, a benchmark for evaluating whether coding agents can build complete, functional software repositories end to end and finds substantial variation in end-to-end repository generation ability, providing strong model differentiation while leaving considerable headroom for future progress.
Hantian Ding, Chloe Bi, Jia-Cheng Zhu et al.· 0 citations
CAT is introduced, a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development.
Bin Hong, Zhen-Chao Zhang, Ji-Yuan He et al.· 0 citations
The findings indicate that service/API grounding is the main bottleneck: Mock is dominated by host, endpoint, and method alignment, while ParamF1 separately reveals parameter-alignment errors.
Shuo Wang, Guo-Dong Fan, Ming Zhu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.