## 5 The Survey Results  
In order to answer RQ3, which addresses the usability of intelligent assistants as perceived by users, we designed a survey questionnaire. Developers with experience using at least one of the intelligent assistants participated in the survey, which was conducted in late 2022/early 2023. The respondents, who had diverse demographic backgrounds and experience in IS development, were invited to the survey via a group on a social network. Out of the participants, **68** individuals with experience in IS development submitted a fully completed questionnaire, and these responses were used for further data analysis. In order to create a profile of respondents in the survey, it was necessary to ask specific questions about the characteristics of the respondents in the survey. The results are presented in Table 5 and Figure 3.  
Table 6 shows the averages, standard deviations, the lowest and highest measured values, and the results of the normality test of the variables that were measured as part of  
Figure 3: Average respondent. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean** & **Standard Deviation** & **MIN** & **MAX** \\ \hline Programming experience [Years] & 8.8 & 7.2 & 2 & 35 \\ Experience with the development of IS [Years] & 6.3 & 7.5 & 1 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics of respondents. the statistical analysis of the survey results. The table presents the NPS value for individuals, and the total NPS value is given in the paragraph below. According to the official **SUS** questionnaire scale, the participants rated the intelligent assistant's usefulness as **65.9** (grade **C**, based on the SUS rating scale), or **ok** but **close to satisfactory**. In our sample, the **internal reliability** or alpha coefficient of the scale was **0.87**. The user experience was assessed as **average**, or **0.97**, which is also consistent with other studies and the official scale for interpreting **UEQ** questionnaire. In our sample, the **internal reliability** or alpha coefficient of the scale was **0.89**. The average participant would suggest using an intelligent assistant to others in approximately **slightly more than two-thirds of cases**. The total NPS value was \(-\)**11**, which means that the participants were **not satisfied** with it, and they felt that it should be improved significantly in order for the participants to recommend it to others. However, **82%** of the participants stated that they **would continue to use the intelligent assistants**. The average participant had at least almost **9** years of experience in programming and around **6.3** years of experience in IS development. Almost **70%** of respondents used intelligent assistants at least occasionally to complete program code. It can be seen from Table 6 that the first three variables (SUS, UEQ-s, and UEQ-p) are **typically distributed**, whereas the rest are **non-normally distributed**. Given the rapid evolution and complexity of the software development landscape, it is imperative to categorise professionals based on their specific expertise and experience. This division is not arbitrary but is rooted in the nuances of programming, the intricacies of IS development, and the specialised knowledge required for intelligent assistants. By distinguishing between the number of years of programming experience, the number of years dedicated to the development of IS, and familiarity with intelligent assistants, we aimed to provide a more granular understanding of the capabilities of the professionals who were included in the survey. Table 7 shows the results of the participants' comparisons according to their **years of programming experience**. The **less experienced group** represents those **without more than 5 years** of programming experience. In the **more experienced group**, we have included those with **6 years** or more of programming experience [47].