Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this regard, Natural Language Processing (NLP) can be used to create various related tools (such as assistants) to help with the documentation process. This paper presents the current state-of-the-art NLP techniques used in the agile development documentation process. A mapping review was done to complete the objective, the search strategy is used to obtain relevant studies from ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Willey. The search results after inclusion and exclusion criteria application left 47 relevant papers identified. These papers were analyzed to obtain the most used NLP techniques and NLP toolkits. The toolkits were also classified by the kind of techniques that are available in each of them. In addition, the behavior of the research area over time was analyzed using the relevant paper found by year. We found that performance measuring methods are not standardized, and, in consequence, the works are not easily comparable. In general, the number of related works and its distribution per year shows a growing trend of the works related to this topic in recent years; this indicates that the adoption of NLP techniques to improve agile methodologies is increasing.

In agile software development, user stories are a common tool for capturing functional requirements, valued for their simplicity and user-centric approach. Nevertheless, their inherent informality can introduce ambiguity, especially when acceptance criteria are lacking or poorly defined. Such ambiguity may hinder early design activities, including the development of UML robustness diagrams. This study investigates the impact of acceptance criteria and developer experience on the accuracy and efficiency of robustness diagram construction.A controlled experiment was conducted with thirty participants, divided into newbie and advanced developers, who were tasked with creating robustness diagrams from user stories both with and without acceptance criteria. Performance was assessed based on task duration and diagram completeness. Statistical analysis (Mann-Whitney U test) revealed that acceptance criteria significantly reduce errors and improve completion times, particularly among newbie developers. Experienced developers consistently produced higher-quality diagrams more efficiently, underscoring the role of expertise as a moderating factor.The findings suggest that well-specified acceptance criteria mitigate ambiguity, facilitating more accurate requirements interpretation and improving early design outcomes. Additionally, the results highlight the value of structured requirement practices in agile methodologies, especially in teams with varying levels of experience. This research advances the understanding of how requirement clarity and developer expertise collectively influence software modeling, providing actionable recommendations for enhancing agile design processes.