Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study

• URL: http://arxiv.org/abs/2510.25016v1
• Date: Tue, 28 Oct 2025 22:29:11 GMT
• Title: Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study
• Authors: Mateen Ahmed Abbasi, Petri Ihantola, Tommi Mikkonen, Niko Mäkitalo,
• Abstract summary: This study introduces the Human-AI RE Synergy Model (HARE-SM)<n>The model integrates AI-driven analysis with human oversight to improve requirements elicitation, analysis, and validation.<n>We outline a multi-phase research methodology focused on preparing RE datasets, fine-tuning AI models, and designing collaborative human-AI.
• Score: 2.195918681143262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The future of Requirements Engineering (RE) is increasingly driven by artificial intelligence (AI), reshaping how we elicit, analyze, and validate requirements. Traditional RE is based on labor-intensive manual processes prone to errors and complexity. AI-powered approaches, specifically large language models (LLMs), natural language processing (NLP), and generative AI, offer transformative solutions and reduce inefficiencies. However, the use of AI in RE also brings challenges like algorithmic bias, lack of explainability, and ethical concerns related to automation. To address these issues, this study introduces the Human-AI RE Synergy Model (HARE-SM), a conceptual framework that integrates AI-driven analysis with human oversight to improve requirements elicitation, analysis, and validation. The model emphasizes ethical AI use through transparency, explainability, and bias mitigation. We outline a multi-phase research methodology focused on preparing RE datasets, fine-tuning AI models, and designing collaborative human-AI workflows. This preliminary study presents the conceptual framework and early-stage prototype implementation, establishing a research agenda and practical design direction for applying intelligent data science techniques to semi-structured and unstructured RE data in collaborative environments.

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