Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.25905 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918411546656768 |
|---|---|
| author | Hess, Anne Vogelsang, Andreas Franch, Xavier Herrmann, Andrea Kopczyńska, Sylwia Rachmann, Alexander |
| author_facet | Hess, Anne Vogelsang, Andreas Franch, Xavier Herrmann, Andrea Kopczyńska, Sylwia Rachmann, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25905 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Opportunities and Limitations of GenAI in RE: Viewpoints from Practice Hess, Anne Vogelsang, Andreas Franch, Xavier Herrmann, Andrea Kopczyńska, Sylwia Rachmann, Alexander Software Engineering 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. |
| title | Opportunities and Limitations of GenAI in RE: Viewpoints from Practice |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2603.25905 |