A Toolkit for Generating Code Knowledge Graphs
Ibrahim Abdelaziz, Julian Dolby, et al.
K-CAP 2021
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.
Ibrahim Abdelaziz, Julian Dolby, et al.
K-CAP 2021
Yutian Yan, Yunhui Zheng, et al.
ICSE 2023
Michael Muller, April Yi Wang, et al.
IUI 2021
Jasmine Shih, Vishal Mohanty, et al.
CHI 2024