# Developing Microservice-Based Applications Using the Silvera Domain-Specific Language
## 3 Silvera
### Silvera Concrete Syntax
#### 3.3.2 Service Registry Declaration
To make sure that the study results were valid, we checked if submitted task implementations were functional. We created a set of automated tests that were performed for every submitted implementation. These tests were used to calculate the task completeness and were not shared with the participants. ### Analysis and Results

In total, 50 people were invited to participate in the survey, of which 24 accepted the invitation. However, only 18 participants completed the survey. Of the participants, 4 were developers with at least 3 years of working experience, and 14 were students in their last year of bachelor studies. As shown in Table 14, the experience of participants varied, with \(\sim\)33% of participants declaring themselves as _relatively experienced_ or _experienced_ in designing software architectures, \(\sim\)17% declaring themselves as _relatively experienced_ or _experienced_ in designed MSA, whereas \(\sim\)5.5% of participants declaring themselves as _relatively experienced_ or _experienced_ with CASE tools. Since we wanted to make sure that Silvera can be easily used by developers inexperienced with MSA, the participants met the study requirements. #### 5.7.1 Performance Results

In Table 15, we show the performance results with regard to effectiveness and efficiency. All study participants implemented the task (Section 5.4) both with Silvera and manually (see Table 15). Participants needed assistance 10 times, resulting in a mean of _0.98_, as depicted in Table 15. All assistance was performed verbally (online), during which the participants were instructed to pause the clock. The mean task completion time when using Silvera was just below 3.5 h. On average, participants implemented the task \(\sim\)124% faster when using Silvera than when implementing the same task manually. We used R language to perform a paired samples \(t\)-test to compare the means of two samples of implementation durations. We tested if Silvera indeed accelerates the implementation of MSAs, and we obtained a \(p\)-value (\(p\)-value = 5.539 \(\times\) 10\({}^{-5}\)) that was less than the significance level (\(\alpha\) = 0.01). The test statistic and effect size values were \(V\) = \(-\)5.185483 and 0.91, respectively. Because the participants' \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Question** & **Inexperienced (\%)** & **Relatively Inexperienced (\%)** & **Medium Experience (\%)** & **Relatively Experienced (\%)** & **Experienced (\%)** \\ \hline Q1: How would you describe your previous experience in designing a software architecture? & 5.55 & 5.55 & 55.55 & 27.77 & 5.55 \\ \hline Q2: How would you describe your previous experience in designing a microservice software architecture? & 33.33 & 33.33 & 16.67 & 11.11 & 5.55 \\ \hline Q3: How would you describe your previous experience with CASE tools? & 50.00 & 38.89 & 5.55 & 5.55 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Percentages of responses (n = 18) for _Experience_ group of questions. expertise in MSAs varied to a great extent, high standard deviation values for the task completion times were expected. The completeness was measured by running the set of automated tests. As depicted in Table 15, both approaches yielded similar results, with Silvera having an average task completion rate of \(\sim\)95% compared to the \(\sim\)91% of the manual approach. While the maximum task completion rate was the same for both approaches, the minimum completion rate was slightly higher for the manual approach. Values for the minimum completion rate were lower than expected because some participants did not name their functions as stated in the task, which caused the failure of some of the automated tests. The _median_ value was the same for both approaches. However, the _mode_ value (the most commonly occurring value) showed that the task completion rate was 100% for the majority of participants when using Silvera.