## 5 Discussion  
Table 6 summarizes the data for the interpretation and the discussion. Having presented result data, it seems quite obvious, that, unlike in a professional-driven experiment, a student-driven experiment with the same setting, resulted in data, that diverges both when we compare them in a student-to-student manner as well as when we compare the data in a student-to-professional manner (see Table 6). Based on the established [PERSON]'s kappa value interpretation [22], it is obvious not only that strong agreement is achieved inside a professional group (highest value, i.e., \"almost perfect agreement\" is reached practically on all evaluated aspects), but also, that the agreement is consistent throughout all quality aspects that we included in the experiment (\"almost perfect agreement\" according to [22]). On the other hand, the [PERSON]'s kappa values for student participants show worse performance in terms of the achieved agreement in practically all aspects. We can also find entities (E30-E33) with one or several aspects where poor agreement (the lowest rate according to [22]) was recorded. To support our findings further, the aggregated values of [PERSON]'s kappa for all entities was also calculated, as shown in Table 7.  
Based on the aggregated [PERSON]'s kappa values in Table 7, it is even more evident that there is a lack of agreement inside the student group (the student group diverges in their answers). As [PERSON]'s kappa values and their established interpretation clearly shows, professionals are consistent in their expert judgments. Furthermore, as shown in Table 7, professionals can reach almost perfect agreement (the highest value 1.0, see Tables 5 and 7) on all given source code quality aspects. In the \"cohesion\" aspect, professionals reached the worst agreement (value 0.8) which is just below \"Almost perfect agreement\" and is the highest value in the interpretation of \"substantial agreement\". \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Entity**} & \multicolumn{6}{c}{**Experts**} & \multicolumn{6}{c}{**Students**} \\ \cline{2-10}  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **  
However, as we will show, the \"cohesion\" aspect was shown to be the most complex, since students also had the lowest agreement on it. The situation, when comparing agreement levels, is different when looking at student groups. As shown in Table 7, almost perfect agreement is found only when we take into account source code size. Furthermore, even in this case, the value is the lowest possible one, to be interpreted as \"almost perfect agreement\" (0.81). In other aspects, agreement barely achieves a substantial level (complexity with a value of 0.65, overall quality with the lowest value for this agreement class-0.61). It is only the \"moderate agreement\" level where we can observe cohesion (0.50) and coupling (0.53). This is why we cannot state that students are consistent in their expert judgments with the source code quality research domain. However, we can identify a particular aspect where student answers do not diverge. Based on this evidence, we can answer research question 1: \"Are participants consistent in expert judgment evaluations regarding the source code quality research domain?\". In the case of professional participants, where strong experience is reported, based on an interpretation of the [PERSON]'s kappa values, we can answer research question 2b as positive. Professionals are consistent in their expert judgment regarding the source code quality research domain. Their level of agreement is almost perfect. In the case of student participants, where domain education seems strong, but a lack of experience is reported, the answer is not straightforward. The interpretation of the [PERSON]'s kappa values shows lower agreement levels for all aspects (research question 2a).