Bang Liu, Yu Chen, et al.
AAAI 2024
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.
Bang Liu, Yu Chen, et al.
AAAI 2024
Gaetano Rossiello, Nhan Pham, et al.
ICLR 2025
Priyam Sahoo, Saurabh Pujar, et al.
ASE 2024
Justin Weisz, Shraddha Kumar, et al.
CHI 2025