用大模型提前识别软件需求中的可持续性问题，提升绿色开发效率
SEER: Sustainability Enhanced Engineering of Software Requirements
- 基于大模型和检索增强生成，从通用分类中提取具体可持续性需求
- 在四个不同领域的项目中验证，能准确识别多样化的可持续性风险
- 适合关注绿色软件开发的团队，尤其适合早期需求阶段优化
软件开发的快速发展带来了环境、技术、社会与经济影响。为实现2030年联合国可持续发展目标，开发者需采纳可持续实践。现有方法多提供高层次指导，实施耗时且依赖团队适应性，且聚焦设计或实现阶段，而可持续性评估应始于需求工程阶段。本文提出SEER框架，在软件开发早期应对可持续性问题。该框架分三步：(i) 从通用分类中识别特定软件产品的可持续性需求（SRs）；(ii) 基于已识别的SRs评估系统需求的可持续性水平；(iii) 优化不满足任何SR的系统需求。框架利用大语言模型的推理能力与代理式RAG（检索增强生成）方法实现。在四个来自不同领域的软件项目上进行实验，使用Gemini 2.5推理模型生成的结果表明，该方法在跨领域环境中准确识别广泛可持续性关切具有显著有效性。
原文摘要 · Abstract (English)
The rapid expansion of software development has significant environmental, technical, social, and economic impacts. Achieving the United Nations Sustainable Development Goals by 2030 compels developers to adopt sustainable practices. Existing methods mostly offer high-level guidelines, which are time-consuming to implement and rely on team adaptability. Moreover, they focus on design or implementation, while sustainability assessment should start at the requirements engineering phase. In this paper, we introduce SEER, a framework which addresses sustainability concerns in the early software development phase. The framework operates in three stages: (i) it identifies sustainability requirements (SRs) relevant to a specific software product from a general taxonomy; (ii) it evaluates how sustainable system requirements are based on the identified SRs; and (iii) it optimizes system requirements that fail to satisfy any SR. The framework is implemented using the reasoning capabilities of large language models and the agentic RAG (Retrieval Augmented Generation) approach. SEER has been experimented on four software projects from different domains. Results generated using Gemini 2.5 reasoning model demonstrate the effectiveness of the proposed approach in accurately identifying a broad range of sustainability concerns across diverse domains.
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