Large Language Models (LLMs) are becoming key in automating and assisting various software development tasks, including text-based tasks in requirements engineering but also in coding. Typically, these models are used to automate small portions of existing tasks, but we present a broader vision to span multiple steps from requirements engineering to implementation using existing libraries. This approach, which we call Semantic API Alignment (SEAL), aims to bridge the gap between a user's high-level goals and the specific functions of one or more APIs. In this position paper, we propose a system architecture where a set of LLM-powered ``agents'' match such high-level objectives with appropriate API calls. This system could facilitate automated programming by finding matching links or, alternatively, explaining mismatches to guide manual intervention or further development. As an initial pilot, our paper demonstrates this concept by applying LLMs to Goal-Oriented Requirements Engineering (GORE), via sub-goal analysis, for aligning with REST API specifications, specifically through a case study involving a GitHub statistics API. We discuss the potential of our approach to enhance complex tasks in software development and requirements engineering and outline future directions for research.

翻译：大型语言模型（LLM）正逐渐成为自动化与辅助各类软件开发任务的关键技术，涵盖需求工程中的文本型任务以及代码编写。通常，这些模型用于自动化现有任务中的少量环节，但我们提出了一种更宏大的愿景，旨在将需求工程到利用现有库实现代码的多个步骤串联起来。这种称为语义API对齐（SEAL）的方法，旨在弥合用户高层目标与一个或多个API具体功能之间的鸿沟。在本立场论文中，我们提出了一种系统架构，该架构由一组基于LLM的"智能体"驱动，用于将此类高层目标与适当的API调用相匹配。该系统可通过发现匹配链接实现自动化编程，或通过解释不匹配情况来引导人工干预或进一步开发。作为初步验证，本文通过将LLM应用于面向目标的需求工程（GORE），借助子目标分析实现与REST API规范的对齐，并基于GitHub统计API的案例研究进行了概念验证。我们讨论了该方法在增强软件开发与需求工程复杂任务方面的潜力，并概述了未来研究方向。