Ansible Lightspeed: A Code Generation Service for IT Automation - Sahoo, Priyam; Pujar, Saurabh; Nalawade, Ganesh; Gebhardt, Richard; Mandel, Louis; Buratti, Luca (IEEE/ACM International Conference on Automated Software Engineering : [proceedings], 2024)
The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been re-leased, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML, given natural language prompt.In this paper, we present the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user edited suggestions, as well as user sentiments analysis. The evaluation is based on data collected for 10,696 real users including 3,910 returning users. The code for Ansible Lightspeed service and the analysis framework is made available for others to use.To our knowledge, our study is the first to involve thousands of users of code assistants for domain-specific languages. We are also the first code completion tool to present N-Day user retention figures, which is 13.66% on Day 30. We propose an improved version of user acceptance rate, called Strong Acceptance rate, where a suggestion is considered accepted only if less than 50% of it is edited and these edits do not change critical parts of the suggestion. By focusing on Ansible, Lightspeed is able to achieve a strong acceptance rate of 49.08% for multi-line Ansible task suggestions. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code completion.