用大模型自动写、评、优化Bash脚本，提升运维效率
ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement
- 基于大模型统一生成、评估与优化Bash脚本
- 在CodeSift和InterCode数据集上提升7-10%生成效果
- 适合SRE工程师快速编写可靠运维脚本
在不断演进的站点可靠性工程（SRE）领域，高效解决站点与云应用问题的需求日益迫切。本文提出一种利用大语言模型（LLMs）实现脚本生成、评估与优化的创新自动化方法。通过大模型能力，显著降低编写与调试脚本的人工成本，提升SRE团队生产力。实验聚焦于SRE常用工具Bash脚本，采用CodeSift数据集（100个任务）和InterCode数据集（153个任务）。结果表明，大模型可高效自动评估与优化脚本，减少对执行环境验证的需求。框架整体脚本生成性能提升7-10%。
原文摘要 · Abstract (English)
In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount. This paper presents an innovative approach to action automation using large language models (LLMs) for script generation, assessment, and refinement. By leveraging the capabilities of LLMs, we aim to significantly reduce the human effort involved in writing and debugging scripts, thereby enhancing the productivity of SRE teams. Our experiments focus on Bash scripts, a commonly used tool in SRE, and involve the CodeSift dataset of 100 tasks and the InterCode dataset of 153 tasks. The results show that LLMs can automatically assess and refine scripts efficiently, reducing the need for script validation in an execution environment. Results demonstrate that the framework shows an overall improvement of 7-10% in script generation.
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