Approaches for Business Process Model Complexity Metrics

Abstract

One of the main purposes for developing business process models (BPM) is to support the communication between the stakeholders in the software development process (domain experts, business process analysts, software developers to name just a few). To fulfill this purpose, the models should be easy to understand and easy to maintain. If we want to create models that are easy to understand, at first we have to define what “easy to understand” means: We are interested in complexity metrics, i.e. measurements that can tell us whether a model is easy or difficult to understand. In the latter case, we may conclude from the metrics that the model should be reengineered, for example by decomposing it into simpler modules. A significant amount of research has been done on the complexity of software programs, and software complexity metrics have been used successfully for purposes like predicting the error rate, estimating maintenance costs or identifying pieces of software that should be re-engineered. In this paper, we discuss how the ideas known from software complexity research can be used for analyzing the complexity of BPMs. To our best knowledge, there is rather few published work about this subject. In 2002, Latva-Koivisto[1] was the first one who suggested to study BPM complexity metrics, but this paper discussed rather simple BPM languages (like process charts) only. Cardoso[2] (whose approach we discuss in section 2.4) was the first author who addressed the problems of measuring the complexity of more expressive BPM languages. Following this pioneering work, other authors have published about this