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ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement

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arxiv 2409.17166 v1 pith:AG2GTDY3 submitted 2024-09-12 cs.SE cs.AI

Oishik Chatterjee , Pooja Aggarwal , Suranjana Samanta , Ting Dai , Prateeti Mohapatra , Debanjana Kar , Ruchi Mahindru , Steve Barbieri
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Eugen Postea Brad Blancett Arthur De Magalhaes
This is my paper · ORCID
classification cs.SEcs.AI
keywords scriptgenerationllmsscriptsassessmentbashdatasetenhancing
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. LLM-Supported Natural Language to Bash Translation

    cs.CL 2025-02 conditional novelty 6.0 of 10

    New 600-pair verified NL2SH test set and an execution-plus-LLM equivalence checker improve Bash-command translation evaluation; parsing and in-context learning give the largest gains for small models.

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