Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development
Mateen Ahmed Abbasi, Petri Ihantola, Tommi Mikkonen, Niko Mäkitalo
TL;DR
The paper addresses persistent Requirements Engineering (RE) challenges—ambiguity, conflicting stakeholder needs, and evolving requirements—by examining how data-driven AI, including NLP, ML, and Generative AI, can augment RE tasks such as elicitation, traceability, and prioritization. It employs a structured three-phase literature review to map traditional RE gaps and evaluate AI-driven solutions, emphasizing metrics like accuracy, scalability, transparency, and ethics. Key contributions include identifying five core RE challenges, analyzing AI’s impact on these challenges, and proposing directions such as Retrieval-Augmented Generation (RAG), fine-tuning, and reinforcement learning (RL) to enhance RE, along with an integration framework and empirical evaluation plan. The work advocates a cautious, human-in-the-loop approach to AI-enabled RE, highlighting the need for trustworthy, explainable, and fair AI tools that can adapt to fast-paced software development while aligning with stakeholder values.
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.