Invited: Automated Code generation for Information Technology Tasks in YAML through Large Language Models - Pujar, Saurabh; Buratti, Luca; Guo, Xiaojie; Dupuis, Nicolas; Lewis, Burn; Suneja, Sahil; Sood, Atin; Nalawade, Ganesh; Jones, Matt; Morari, Alessandro; Puri, Ruchir (2023 60th ACM/IEEE Design Automation Conference (DAC), 2023)
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, received far less attention, despite involving many active developers and being an essential component of modern cloud platforms. This work focuses on the generation of Ansible YAML, a widely used markup language for IT Automation. We present Ansible Wisdom, a natural-language to Ansible YAML code generation tool, aimed at improving IT automation productivity. Results show that Ansible Wisdom can accurately generate Ansible script from natural language prompts with performance comparable or better than existing state of the art code generation models.