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，从业者和研究者见证了生成式人工智能的快速且广泛的实施。从业者和研究者每日提出的新模型使得快速采用成为可能。文本生成式人工智能的能力使全球研究人员能够探索新的生成场景，简化并加速所有耗时的文本生成与分析任务。动机。我们领域内出版物数量的指数级增长，加之数字图书馆带来的信息可访问性提高，使得进行系统性文献综述和映射研究成为一项耗费精力和时间不敏感的任务。源于这一挑战，我们研究并展望了生成式人工智能在基于证据的软件工程中的角色。未来方向。基于我们当前的调查，我们将通过创建并实证验证一套全面的模型来跟进这一愿景，以有效支持基于证据的软件工程研究者。