External Cooperation: Osthus GmbH
Research in constructing software, in other terms software engineering, already exists for decades and over the years several approaches emerged. Ranging from procedural, functional until object oriented approaches, creating a model of the system to be build is a cornerstone of every approach. But, all software systems, which are modeled and developed with these approaches, deal with single pieces of software or software systems for narrow scopes (metaphor: e.g. for departments in companies). Consequently, one step in abstraction deals with connecting these software systems to workflows. This adds a goal oriented perspective and links several systems together to (e.g.) support departments working together in a static, but automated way. What remains is the importance of models in a sense of abstractions from the real world. The next step in abstraction adds business goals as a motivation and concerns the whole company or even several companies.
Hence, business process modeling enlarges the perspective with business goals and mainly adds the aspects of agile business processes and business goals. This sounds like documentation at first but committing to business processes actually burden engineers with severe architectural constrains. Moreover, demands emerged which want to have these business processes automated. As a consequence, business processes are required to be flawless in many different ways.
On the one side, a lot of research has been undertaken in the field of metrics for business process models. On the other side, a lot of quality models exist on a very high level of abstraction. But, only very few researchers ventured to establish a link between metrics and models. This is due to the gap perceived small but actually it is immense.
So, the initial questions are: What can we do with the metrics invent so far? What do the numbers mean with respect to a given quality model? Which metrics are missing? And, combined with simulations on business process models - which information we can glean? And, finally, is there interesting information derivable from historical information of models?
These simple questions turn out to be challenging since lot of the foundations are still missing. Sure, there are approaches to run simulations on business process models but these approaches are very immature and barely afford the information needed.