# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 3 Research Method  
In the context of SAFe, this paper aims to investigate and evaluate the current state of AI-driven assistants. Through a thorough assessment of related literature, we aim to highlight current research, improvements, and applications of AI in supporting agile techniques in large-scale software development environments. By analyzing the strengths and weaknesses of these AI-driven assistants critically, we aim to discover all the possibilities for development and create new opportunities for further studies. The research method used was a systematic literature review (SLR), which will provide a summary of relevant material accessible from the digital libraries listed in Table 1. Employing the systematic review methodology outlined by [PERSON] [16], we ensured a meticulous and exhaustive approach throughout our research process. Based on the results obtained using this method, we will answer the research questions presented in Section 1. After forming the selection of digital libraries, we defined a set of search strings for the purpose of addressing the defined research questions. The selected search strings are shown in Table 2.  
We also defined inclusion and exclusion criteria, which were crucial for achieving a thorough assessment in this paper; they served as key filters that guided the selection of relevant research that matches our field of interest from the wide range of published literature. The set inclusion and exclusion criteria are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Digital Library** & **Web Address** \\ \hline IEEE Xplore & [[http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/)]([http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/)) \\ ScienceDirect & [[https://www.sciencedirect.com/](https://www.sciencedirect.com/)]([https://www.sciencedirect.com/](https://www.sciencedirect.com/)) \\ SpringerLink & [[https://link.springer.com/](https://link.springer.com/)]([https://link.springer.com/](https://link.springer.com/)) \\ ACM Digital Library & [[https://dl.acm.org/](https://dl.acm.org/)]([https://dl.acm.org/](https://dl.acm.org/)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chosen digital libraries. \begin{table}
\begin{tabular}{c c} \hline \hline
**Search String** & **Purpose** \\ \hline \textascial*\& AND \textasciule*\& AND (\textasciule*\& OR \textasciule*\& Scaled \\ Agile Framework*) AND (\textasciule*\& OR & agile development method. We address **RQ1** \\ \textasciule*\& OR \textasciule*\& \textasciule*\& **RQ3** partially. \\ \hline \textasciule*\& AND \textasciule*\& AND (\textasciule*\& OR \textasciule*\& Artificial \\ intelligence* OR \& ML* OR \&machine learning*) & \\ AND (\textasciule*\& ON \textasciule*\&  
With the defined criteria, we began the SLR process. The literature review was conducted in mid-2023 and consisted of six stages, which are presented in Figure 1.  
1. **Initial search. ** In the first phase of the SLR process, we obtained **2159** items. In the digital libraries ScienceDirect, SpringerLink, and ACM Digital Library, we selected papers in the fields of Computer Science, Informatics, and Software Engineering, and we filtered the search strings according to the set inclusion criteria. 2. **Duplicate removal. ** The process of duplicate removal followed, where all results were normalized in a single literature list and duplicates were eliminated. We identified 313 instances where papers showed up multiple times, and, after their removal, the literature list was reduced to **1846** items. 3. **Title- and keyword-based screening. ** In this stage, we eliminated the largest amount of papers; out of 1846 items, we reduced the literature list to **112** items. This significant change is attributed to the ever-growing interest in AI as a compelling research domain, leading to an influx of papers and case studies across various sectors like medicine and construction.