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 IT
automation, have not received much attention. Ansible is one such YAML-based IT
automation-specific language. Red Hat Ansible Lightspeed with IBM Watson Code
Assistant, further referred to as Ansible Lightspeed, is an LLM-based service
designed explicitly for natural language to Ansible code generation.
In this paper, we describe the design and implementation of the Ansible
Lightspeed service and analyze feedback from thousands of real users. We
examine diverse performance indicators, classified according to both immediate
and extended utilization patterns along with user sentiments. The analysis
shows that the user acceptance rate of Ansible Lightspeed suggestions is higher
than comparable tools that are more general and not specific to a programming
language. This remains true even after we use much more stringent criteria for
what is considered an accepted model suggestion, discarding suggestions which
were heavily edited after being accepted. The relatively high acceptance rate
results in higher-than-expected user retention and generally positive user
feedback. This paper provides insights on how a comparatively small, dedicated
model performs on a domain-specific language and more importantly, how it is
received by users.

Red Hat Ansible Lightspeed with IBM Watson Code Assistant is an LLM-based service that generates natural language to Ansible code. The analysis shows higher user acceptance rates compared to general-purpose tools, leading to positive user feedback and retention.

Ansible 极速：IT 自动化的代码生成服务

Ansible Lightspeed: A Code Generation Service for IT Automation

Large language models (LLMs) have recently become remarkably good at
improving developer productivity for high-resource programming languages. These
models use two kinds of data: large amounts of unlabeled code samples for
pretraining and relatively smaller amounts of labeled code samples for
fine-tuning or in-context learning. Unfortunately, many programming languages
are low-resource, lacking labeled samples for most tasks and often even lacking
unlabeled samples. Therefore, users of low-resource languages (e.g., legacy or
new languages) miss out on the benefits of LLMs. Cross-lingual transfer
learning uses data from a source language to improve model performance on a
target language. It has been well-studied for natural languages, but has
received little attention for programming languages. This paper reports
extensive experiments on four tasks using a transformer-based LLM and 11 to 41
programming languages to explore the following questions. First, how well
cross-lingual transfer works for a given task across different language pairs.
Second, given a task and target language, how to best choose a source language.
Third, the characteristics of a language pair that are predictive of transfer
performance, and fourth, how that depends on the given task.

大型语言模型（LLMs）在提高高资源编程语言的开发者生产力方面表现出色。然而，许多低资源编程语言由于缺乏标记样本而无法受益于 LLMs，本研究通过使用源语言数据来提高目标语言的模型性能，并针对 4 个任务进行了广泛实验，以探讨交叉语言迁移学习的工作原理和最佳选择源语言的方式。