Opportunities and Limitations of GenAI in RE: Viewpoints from Practice

2026-03-26

AI Summary

This study addresses the current lack of empirical evidence on the applicability and limitations of generative artificial intelligence (GenAI) in requirements engineering (RE) from an industrial practice perspective. In collaboration with the IREB AI & RE Special Interest Group, the project presents the first large-scale survey of RE practitioners, employing a mixed-methods approach that integrates questionnaire data, quantitative statistical analysis, and qualitative content analysis. It systematically investigates GenAI’s use cases, perceived benefits, key challenges, and skill gaps across all core RE phases—requirements elicitation, analysis, specification, validation, and management. By bridging the evidence gap between academia and industry, this work provides empirically grounded insights to inform future research on intelligent RE and guide enterprise strategies for AI integration in requirements engineering practices.

Abstract

Context and motivation: With the rapid advancement of AI technologies, there is an increasing need to understand how AI can be effectively integrated into RE processes. In recent years, several studies have explored the potential and challenges of applying GenAI to support or even automate RE-related activities. Question/problem: Despite the existing body of knowledge on AI's potential for supporting RE activities, there is limited evidence on its practical applicability and limitations from an industry perspective. Principal ideas/results: To address this gap, we conducted a survey with RE practitioners in collaboration with the IREB Special Interest Group on AI & RE. In addition to describing our research methodology and survey design, we present insights from our quantitative and qualitative data analyzes. These insights include practitioners' perspectives on current usage scenarios, concerns, experiences-both positive and negative-as well as training needs related to using GenAI in requirements elicitation, analysis, specification, validation, and management. Contribution: This study provides empirical evidence on the practical use of GenAI in RE, offering insights into its benefits, challenges, and training needs. The findings inform future research and industry strategies, guiding effective AI integration and skill development for improved RE processes and results.

Problem

• GenAI
• Requirements Engineering
• Practical Applicability
• Industry Perspective
• Limitations

Innovation

• Generative AI
• Requirements Engineering
• Empirical Study
• Industry Survey
• AI Integration