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The recent improvement in code generation capabilities due to the use oflarge language models has mainly benefited general purpose programminglanguages. Domain specific languages, such as the ones used for IT Automation,have received far less attention, despite involving many active developers andbeing an essential component of modern cloud platforms. This work focuses onthe generation of Ansible-YAML, a widely used markup language for ITAutomation. We present Ansible Wisdom, a natural-language to Ansible-YAML codegeneration tool, aimed at improving IT automation productivity. Ansible Wisdomis a transformer-based model, extended by training with a new datasetcontaining Ansible-YAML. We also develop two novel performance metrics for YAMLand Ansible to capture the specific characteristics of this domain. Resultsshow that Ansible Wisdom can accurately generate Ansible script from naturallanguage prompts with performance comparable or better than existing state ofthe art code generation models. In few-shot settings we asses the impact oftraining with Ansible, YAML data and compare with different baselines includingCodex-Davinci-002. We also show that after finetuning, our Ansible specificmodel (BLEU: 66.67) can outperform a much larger Codex-Davinci-002 (BLEU: 50.4)model, which was evaluated in few shot settings.

Automated Code generation for Information Technology Tasks in YAML through Large Language Models