SEER: Sustainability Enhanced Engineering of Software Requirements
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Abstract (arXiv)
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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References · 15
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 431
- 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
- 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
- 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 246
- 2024Generative AI for Requirements Engineering: A Systematic Literature Reviewcited by 11
- 2025Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospectscited by 9
- 2024RECOVER: Toward Requirements Generation from Stakeholders' Conversationscited by 6
- 2025Measuring Determinism in Large Language Models for Software Code Reviewcited by 6
- 2022TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirementscited by 3
- 2025Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Studycited by 3
3 more
- 2024The Sustainability Assessment Framework Toolkit: A Decade of Modeling Experiencecited by 2
- 2025From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industrycited by 1
- 2025Perspectives, Needs and Challenges for Sustainable Software Engineering Teams: A FinServ Case Studycited by 1
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- 2026TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development
- 2026On the Viability of Requirements Generation From Code: An Experience Report ↗
- 2024Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks
- 2025Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements ↗