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Among the thriving ecosystem of cloud computing and the proliferation ofLarge Language Model (LLM)-based code generation tools, there is a lack ofbenchmarking for code generation in cloud-native applications. In response tothis need, we present CloudEval-YAML, a practical benchmark for cloudconfiguration generation. CloudEval-YAML tackles the diversity challenge byfocusing on YAML, the de facto standard of numerous cloud-native tools. Wedevelop the CloudEval-YAML benchmark with practicality in mind: the datasetconsists of hand-written problems with unit tests targeting practicalscenarios. We further enhanced the dataset to meet practical needs byrephrasing questions in a concise, abbreviated, and bilingual manner. Thedataset consists of 1011 problems that take more than 1200 human hours tocomplete. To improve practicality during evaluation, we build a scalableevaluation platform for CloudEval-YAML that achieves a 20 times speedup over asingle machine. To the best of our knowledge, the CloudEval-YAML dataset is thefirst hand-written dataset targeting cloud-native applications. We present anin-depth evaluation of 12 LLMs, leading to a deeper understanding of theproblems and LLMs, as well as effective methods to improve task performance andreduce cost.

CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation