## 3 Methodology  
### Method  
The PICO approach (Population, Intervention, Comparison, Outcome) has proven to be an essential tool in scientific research due to its ability to structure and organize complex questions in a clear and systematic manner, as shown in Table 1. When applied rigorously, the PICO methodology allows for a detailed and objective analysis of the key elements of a study, avoiding the subjective biases that are often present in other approaches, such as narrative reviews [24]. The PICO methodology has not only stood out in fields such as healthcare and medicine but has also demonstrated its value in areas such as software engineering, automation processes, and agile development practices, as evidenced by recent research (to include updated references). It facilitates the formulation of precise questions and the selection of relevant studies, ensuring that data are analyzed consistently and comparably [25]. In this study, the PICO methodology is applied to structure the analysis of the integration of Requirements Engineering (RE) practices and DevOps in the development of Robotic Process Automation (RPA) systems, with a particular focus on optimizing human-computer interaction. The adaptation of this methodology to the context of Requirements Engineering, DevOps, and RPA provides a systematic approach to understanding how the integration of these components impacts the lifecycle of an automation project, with special attention to user experience and the performance of the automation system [26; 27]. In the present work, the \"population\" refers to organizations that have already implemented or are planning to adopt RPA solutions via the integration of RE and DevOps \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline P & (Population) Group of interest in the study. \\ \hline I & (Intervention) Exposure or factor analyzed. \\ \hline C & (Comparison) Comparison with another intervention or control group. \\ \hline O & (Outcome) Expected or measured result of the study. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PICO model structure. practices, with a particular focus on improving human interaction with the automated system. Defining this population precisely is crucial to ensure that the results are relevant and applicable to industrial and business contexts that face similar challenges related to process automation and the development of user-centric systems. By examining companies that apply advanced RE and DevOps practices to create more effective, user-centric RPA solutions, a deeper understanding of development processes and their impacts on organizational performance and user experience can be gained. The \"intervention\" in this study involves the implementation of combined RE and DevOps practices in the development of RPA systems, with the aim of optimizing human-computer interaction and improving the efficiency of automated processes. This intervention is based on growing evidence that integrating these practices can not only increase the efficiency and quality of RPA system development but also enhance the user experience by creating intuitive and highly functional systems. The \"comparison\" in this study will be conducted between the manual execution of a process within a specific department of a company and the implementation of the framework developed in this work, which incorporates integrated RE and DevOps practices into the automation of RPA projects for the same process. This analysis will evaluate the differences before and after the framework's application, taking into account the gains and benefits achieved through automation. The comparison aims to identify improvements in the process lifecycle efficiency and the advantages realized following the framework's implementation. The \"outcome\" expected from this study is to demonstrate that integrating RE and DevOps practices into RPA projects contributes to more efficient development, enhancing both human-computer interaction and operational outcomes.