An Empirical Comparison of Code Generation Approaches for Ansible

Abstract

The rapid proliferation of LLM-based programming assistants has enabled fast and accurate automatic code generation for general purpose programming languages. Domain-specific languages like Ansible, a DSL for IT Automation, have seen a lack of support despite being critical to many fields, due to limited public-domain code for training models and a lack of interest from tool developers. To address this issue, we collect a novel dataset of permissively licensed Ansible code, and use it to create Warp, an LLM for code fine-tuned to produce Ansible tasks from a natural language prompt. We evaluate state-of-the-art tools for LLM-based code generation models, comparing multiple common strategies, including fine-tuning base models on Ansible code and retrieval-augmented-generation using documentation, in order to understand challenges with existing methodology and identify future research directions to enable better code generation for DSLs.