## 3 Experiments
### Results
#### 3.2.2 Analysis of Full-Model Metrics  
We have performed a second analysis using the full-model metrics calculated for the model involved in each exercise and, as in the previous scenario, for the three poolings of exercises. Except for the metrics, the statistical analysis considers the same variables: fixed effect and random effect. Table 5 summarizes the results of the metrics whose significance level was below the threshold value (\(\rho<0.05\)) in the statistical test and, thus, showing a statistically significant correlation in, at least, one of the three above mentioned cases. The observed outcomes are summarized below.  
In this case, most of the metrics (14 out of 16) fall under the categories density and partitionability. For the metrics under the size category, diameter (_Diam_) is the only one showing a significant correlation for the pooling with all exercises. Note that the metrics under this category usually compute the aggregation as the sum of the metric over all the graphs in the model, except diameter (_Diam_) which applies a weighted average. For the metrics falling under the connector interplay category, only the number of preconditions (\(S_{\text{Precond}}\)) metric is showing a statistical correlation and just for the analysis exercises. None of the full-model metrics under the concurrency category show a significant correlation.  
All the metrics within the density category are included in the table, which may demonstrate that densities are of special relevance. This is even more apparent in the modifiability exercises, where seven out of the nine metrics within the density category show a significant correlation in one or more of the three poolings of exercises. It is of special relevance that the three full-model metrics we specifically defined for PROforma models--percentage of single node plans (\(p1\%\)), average size of a plan (\(\bar{t}\)), and percentage of plans whose size is over the average size (\(p50\%\))--are significant or show a significance value slightly higher than the threshold, except for the percentage of single node plans (\(p1\%\)) in modifiability exercises.  
Almost all the metrics within the partitionability category indicate a significant correlation for the pooling including all the exercises. The metrics depth (\(\Lambda\)) and model depth (\(\Upsilon\)) also show a significant correlation for the pooling including the analysis exercises, whereas the separability (\(\Pi\)), sequentiality (\(\Xi\)), and structuredness (\(\phi\)) also show a significant correlation for the last pooling of exercises, the one with the modifiability exercises. Only the metric structuredness (\(\phi\)) shows a significant correlation in the three poolings of exercises. This suggests that the metrics capturing the complexity on the structure of the full model, such as separability (\(\Pi\)), sequentiality (\(\Xi\)), and structuredness (\(\phi\)), may somehow indicate  
Figure 4: Graph “_adult assessment_” of Asthma CIG. The 73.33% of analysis exercises and 66.67% of modifiability exercises were correctly solved.  
how error-prone the model is in the modifiability exercises, whereas the ones indicating the depth are of special relevance for the analysis exercises.