Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development

2025-10-05

AI Summary

Traditional requirements engineering (RE) faces significant challenges in AI-native software development—including ambiguous requirements, stakeholder conflicts, and difficulty managing dynamic requirement evolution—while the integration of AI introduces novel concerns such as ethical risks, algorithmic bias, and lack of transparency. To address these issues, this paper proposes a new paradigm: Human-Centered AI-Augmented Requirements Engineering (HCAI-RE), which systematically integrates human-AI collaboration mechanisms, transparent decision-making methods, and an ethics-aware governance framework to support requirement elicitation, validation, and evolution management. Leveraging industry-academia-government collaboration, we develop a trustworthy, adaptive AI-RE integration framework capable of operating effectively in dynamic environments. The paper not only identifies key opportunities and fundamental challenges in AI-augmented RE but also establishes a theoretically grounded, empirically informed methodology with actionable implementation pathways—thereby advancing the development of efficient, reliable, and ethically sound AI-enhanced requirements engineering systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Requirement Engineering (RE) is the foundation of successful software development. In RE, the goal is to ensure that implemented systems satisfy stakeholder needs through rigorous requirements elicitation, validation, and evaluation processes. Despite its critical role, RE continues to face persistent challenges, such as ambiguity, conflicting stakeholder needs, and the complexity of managing evolving requirements. A common view is that Artificial Intelligence (AI) has the potential to streamline the RE process, resulting in improved efficiency, accuracy, and management actions. However, using AI also introduces new concerns, such as ethical issues, biases, and lack of transparency. This paper explores how AI can enhance traditional RE practices by automating labor-intensive tasks, supporting requirement prioritization, and facilitating collaboration between stakeholders and AI systems. The paper also describes the opportunities and challenges that AI brings to RE. In particular, the vision calls for ethical practices in AI, along with a much-enhanced collaboration between academia and industry professionals. The focus should be on creating not only powerful but also trustworthy and practical AI solutions ready to adapt to the fast-paced world of software development.

Problem

• Addressing persistent challenges in Requirements Engineering like ambiguity
• Exploring AI automation for labor-intensive RE tasks and prioritization
• Ensuring ethical trustworthy AI solutions for stakeholder collaboration

Innovation

• AI automates labor-intensive requirement engineering tasks
• AI supports requirement prioritization and stakeholder collaboration
• AI solutions focus on trustworthy and practical adaptation