The second paper in this section on modeling guidelines was a review of modularity in process models, by Hajo Reijers and Jan Mendling. This was focused on factors related to modeling, including methodology, language and tools, and how they affect model quality; the goal being to provide guidance to process modelers for creating better models.
He first showed a general definition of model quality, but pointed out that they focused on error occurrence and understandability as measures of quality. Both errors and understandability are impacted by model size — bigger models have more errors and are less understandable — but density, average connector degree, cross-connectivity, and modeler education (but not education in a specific modeling technique) also impact these factors.
Looking specifically at modularity — the design principle of breaking down a process model into independently managed subprocesses — they hypothesized that use of modularization does not impact understandability. They created an experiment that showed participants one of two versions of two large process models (more than 100 tasks): one with subprocesses, the other flattened into a single process model. They then tested the subject’s understanding of the processes by asking 12 questions for each of the models; these were consultants experienced in process modeling, hence are accustomed to working with process models and would understand the visual syntax. What they found is that the average % of correct answers to the questions is higher for the modular than the flattened version, but for one of the models the difference was not statistically significant, whereas with the other, it was statistically significant.
This disproved their hypothesis, since modularity was important in model understandability one of the two complex models, but raised the question of why it was important for one of the models but not the other. The process with improved understandability on modularization had more subprocesses (hence was more modularized) than the one that didn’t, presenting a new hypothesis for future testing. They also found some correlation between success at answering “local” questions (those related to portions of the process rather than the overall process) and the degree of modularization.
Their conclusions:
- Modularity in a process model appears to have a positive connection with its understandability
- The effect manifests itself in large models if modularity is applied to a sufficiently high extent
- Modularity seems to support comprehension that requires insight into local parts of the model
In the future, they will be relating this work to semi-automatic modularization of work.
At the risk of over simplification, isn’t this obvious? At least from the end user’s perspective?
Dennis, I agree that this is intuitively obvious, but the whole point of research and experimentation is to test these hypotheses in order to confirm them, and also to gain a better understanding of the factors influencing the effect. Likely their research will lead to a better understanding of the correlation between complexity and understandability, which in turn can lead to things such as automated creation of subprocesses when a process reaches a certain level of complexity.