{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cloudeval-yaml-a-practical-benchmark-for","title":"CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation","arxiv_id":"2401.06786","date":"2023-11-10","proceeding":null,"authors":["Yifei Xu","Yuning Chen","Xumiao Zhang","Xianshang Lin","Pan Hu","Yunfei Ma","Songwu Lu","Wan Du","Zhuoqing Mao","Ennan Zhai","Dennis Cai"],"abstract":"Among the thriving ecosystem of cloud computing and the proliferation of Large Language Model (LLM)-based code generation tools, there is a lack of benchmarking for code generation in cloud-native applications. In response to this need, we present CloudEval-YAML, a practical benchmark for cloud configuration generation. CloudEval-YAML tackles the diversity challenge by focusing on YAML, the de facto standard of numerous cloud-native tools. We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios. We further enhanced the dataset to meet practical needs by rephrasing questions in a concise, abbreviated, and bilingual manner. The dataset consists of 1011 problems that take more than 1200 human hours to complete. To improve practicality during evaluation, we build a scalable evaluation platform for CloudEval-YAML that achieves a 20 times speedup over a single machine. To the best of our knowledge, the CloudEval-YAML dataset is the first hand-written dataset targeting cloud-native applications. We present an in-depth evaluation of 12 LLMs, leading to a deeper understanding of the problems and LLMs, as well as effective methods to improve task performance and reduce cost.","url_abs":"https://arxiv.org/abs/2401.06786v1","url_pdf":"https://arxiv.org/pdf/2401.06786v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cloudeval-yaml-a-practical-benchmark-for","repo_url":"https://github.com/alibaba/cloudeval-yaml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"llm-real-life-tasks","task_name":"LLM real-life tasks"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/benchmarking-on-cloudeval-yaml","task":"Benchmarking","dataset":"CloudEval-YAML","model":"GPT-4 Turbo","rank_in_archive_order":1,"of":1,"metrics":{"ACC":"0.561"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}