用大模型辅助产品线功能规划，提升效率与可行性判断。
Towards LLM-Enhanced Product Line Scoping
- 通过自然语言交互让大模型评估功能方案
- 在智能家居场景验证了可行性与效率提升
- 适合需快速迭代产品线的工程团队
产品线规划旨在确定产品线应包含的功能和配置集合，即为配置目的提供选项。在此背景下，关键任务是平衡商业相关性与技术可行性。传统产品线规划依赖正式的功能模型，需要人工分析，耗时较长。本文探讨了大型语言模型（LLMs）如何通过基于自然语言交互的规划流程，支持产品线规划任务。以智能家居领域的一个实际案例为例，我们展示了如何利用大模型评估不同功能模型方案的优劣。同时，讨论了将大模型与产品线规划集成所面临的开放研究挑战。
原文摘要 · Abstract (English)
The idea of product line scoping is to identify the set of features and configurations that a product line should include, i.e., offer for configuration purposes. In this context, a major scoping task is to find a balance between commercial relevance and technical feasibility. Traditional product line scoping approaches rely on formal feature models and require a manual analysis which can be quite time-consuming. In this paper, we sketch how Large Language Models (LLMs) can be applied to support product line scoping tasks with a natural language interaction based scoping process. Using a working example from the smarthome domain, we sketch how LLMs can be applied to evaluate different feature model alternatives. We discuss open research challenges regarding the integration of LLMs with product line scoping.
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