Context. In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence. The daily availability of new models proposed by practitioners and researchers has enabled quick adoption. Textual GAIs capabilities enable researchers worldwide to explore new generative scenarios simplifying and hastening all timeconsuming text generation and analysis tasks. Motivation. The exponentially growing number of publications in our field with the increased accessibility to information due to digital libraries makes conducting systematic literature reviews and mapping studies an effort and timeinsensitive task Stemmed from this challenge we investigated and envisioned the role of GAIs in evidencebased software engineering. Future Directions. Based on our current investigation we will follow up the vision with the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchers

翻译：背景：在不到一年的时间里，从业者和研究人员见证了生成式人工智能的迅速普及与广泛应用。由从业者和研究人员提出的新模型每日涌现，推动了技术的快速采用。文本生成式人工智能的能力使全球研究者能够探索新的生成场景，简化并加速了所有耗时的文本生成与分析任务。动机：本领域出版物数量呈指数级增长，加之数字图书馆带来的信息可及性提升，使得开展系统性文献综述与图谱研究成为一项耗费大量精力且时间敏感的任务。基于这一挑战，我们研究并展望了生成式人工智能在循证软件工程中的角色。未来方向：基于当前研究，我们将持续推进这一愿景，通过创建并实证验证一套全面的模型体系，以有效支持循证软件工程领域的研究者。