## 5 Conclusions  
In this research work, we aimed at providing a comprehensive set of metrics that allow to measure model quality aspects of PROforma CIG models. Although there are many proposals for quality assurance in the field of software and business process models, to the best of our knowledge there are no studies dealing with the quality of CIG models in terms of understandability and modifiability. First, we have proposed metrics inspired in BPM ones to include common process modeling characteristics. Second, we have considered specific features of PROforma to define new metrics that capture distinctive aspects of this CIG language. These include metrics that take into account the modularity and hierarchical decomposition aspects of PROforma models. Finally, we have carried out an empirical validation of these metrics as quality indicators. To this purpose, we have conducted an experiment and carried out a statistical analysis of its results that are presented as a part of this research work. We can conclude that the metrics proposed in this paper can be used as indicators of the understandability, and thereby maintainability, of PROforma CIGs whenever the difficulty of the task is considered. We have observed that when single graphs are taken into consideration, practically all statistically significant metrics belong to the ones inspired in the BPM metrics. However, the newly defined metrics for PROforma prevail as statistically significant when the whole model is considered, which means that they better characterize its complexity. Another observation related to single-graph metrics is that the statistically significant metrics when analysis tasks are considered are disjoint from the significant metrics in the case of modifiability tasks, in most of the cases. Concretely, the single-graph metrics under the categories size, connector interplay, and concurrency appear to be good indicators when solving analysis exercises, whereas the single-graph metrics in the density category seem to be significant in solving modifiability exercises. In the case of the full-model metrics, and for modifiability tasks, the statistically significant metrics are distributed among density and partitionability categories. Still, many of the full-model metrics that we have proposed show correlation for some of the poolings. In light of the above, we consider that the proposed metrics can be of great value, as they capture the PROforma-specific features in addition to those inspired by the general-purpose process model metrics in the literature. We believe that the metrics we have defined are generic enough, and thus we foresee that they could also be a valuable contribution to CIG languages other than PROforma. Notably, a number of metrics (including full-model ones) could be easily adapted and applied to CIG languages following the TNM approach,as PROforma does. As mentioned earlier, this approach is shared by many CIG languages. Furthermore, decision-related metrics could also be generalized, as PROforma's decision model has been adopted by several CIG languages [14]. One limitation of our work lies in the experimental setting, concretely in that the experiment has not particularly been large-scale considering the number of subjects and guideline models. Despite this, we consider that the results we have obtained constitute a valuable contribution from which further research can be undertaken. As future work, a more comprehensive experiment, including more subjects as well as more guidelines and exercises, could be devised to increase the reliability and stability of our results. Note that we do not consider that the lack of participation of clinician subjects in our study should be regarded as a limitation. In our view, we would have obtained a similar outcome if clinicians with the minimum PROforma training required had participated in the study.