Insights from the Usage of the Ansible Lightspeed Code Completion Service
<|reference_start|>Insights from the Usage of the Ansible Lightspeed Code Completion Service: 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 released, 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.   This paper first presents 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 rejected suggestions, as well as analysis of user sentiments. The analysis 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 in evaluating code assistants for domain-specific languages. We propose an improved version of user acceptance rate and we are the first code completion tool to present N-Day user retention figures. 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 services for domain-specific languages in particular and programming languages in general.<|reference_end|>
@article{sahoo2024insights,
    title={Insights from the Usage of the Ansible Lightspeed Code Completion
  Service},
    author={Priyam Sahoo, Saurabh Pujar, Ganesh Nalawade, Richard Gebhardt, Louis
  Mandel, Luca Buratti},
    journal={arXiv preprint arXiv:2402.17442},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17442},
    primaryClass={cs.SE cs.AI cs.PL}
}