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

2025-10-28

AI Summary

Traditional requirements engineering (RE) relies on inefficient and error-prone manual processes, while existing AI-based approaches—though improving automation—suffer from algorithmic bias, limited explainability, and ethical risks. To address these challenges, this paper proposes HARE-SM, a human-AI collaborative RE framework that systematically integrates large language models (LLMs), natural language processing (NLP), and generative AI across multi-stage demand elicitation, analysis, and validation. HARE-SM establishes a dynamic balance between AI-driven automation and expert human oversight, emphasizing explainable AI design, transparent decision-making mechanisms, and proactive bias mitigation. We have completed formal framework modeling and implemented an initial prototype, thereby defining a research roadmap toward trustworthy AI-augmented RE. This work contributes a novel paradigm that bridges theoretical rigor and practical feasibility for intelligent requirements engineering.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Knowledge Engineering
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Developing a human-AI synergy model for requirements engineering
• Addressing algorithmic bias and explainability in AI-driven RE
• Integrating AI analysis with human oversight for RE processes

Innovation

• Human-AI RE Synergy Model integrating AI analysis with oversight
• Fine-tuning AI models and designing collaborative human-AI workflows
• Emphasizing ethical AI use through transparency and bias mitigation