The advancement of Large Language Models (LLM) has also resulted in an
equivalent proliferation in its applications. Software design, being one, has
gained tremendous benefits in using LLMs as an interface component that extends
fixed user stories. However, inclusion of LLM-based AI agents in software
design often poses unexpected challenges, especially in the estimation of
development efforts. Through the example of UI-based user stories, we provide a
comparison against traditional methods and propose a new way to enhance
specifications of natural language-based questions that allows for the
estimation of development effort by taking into account data sources,
interfaces and algorithms.

大型语言模型在软件设计中的应用，特别是基于 LLM 的人工智能代理对开发工作量的评估提出了挑战，通过 UI 界面用户故事的比较，提出了一种改进自然语言问题规范的方法，考虑了数据源、接口和算法来评估开发工作量。

基于大型语言模型的智能界面的软件项目工作量和规模估算

Effort and Size Estimation in Software Projects with Large Language  Model-based Intelligent Interfaces

With recent advancements in natural language processing, Large Language
Models (LLMs) have emerged as powerful tools for various real-world
applications. Despite their prowess, the intrinsic generative abilities of LLMs
may prove insufficient for handling complex tasks which necessitate a
combination of task planning and the usage of external tools. In this paper, we
first propose a structured framework tailored for LLM-based AI Agents and
discuss the crucial capabilities necessary for tackling intricate problems.
Within this framework, we design two distinct types of agents (i.e., one-step
agent and sequential agent) to execute the inference process. Subsequently, we
instantiate the framework using various LLMs and evaluate their Task Planning
and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings
and challenges, our goal is to provide a helpful resource for researchers and
practitioners to leverage the power of LLMs in their AI applications. Our study
emphasizes the substantial potential of these models, while also identifying
areas that need more investigation and improvement.

通过设计两种不同类型的代理进程（即一步代理和连续代理），我们在 LLM 的基础上实例化了一个结构化框架，评估了它们在 typcial 任务的任务规划和工具使用能力，并突出了这些模型的巨大潜力以及需要进一步研究和改进的领域。