The Impact of Sequential and Circumstantial Changes on Process Models

While process modeling has become important for documenting business operations and automating workflow execution, there are serious issues with efficiently and effectively creating and modifying process models. While prior research has mainly investigated process model comprehension, there is hardly any work on maintainability of process models. Cognitive research into software program comprehension has demonstrated that imperative programs are strong in conveying sequential information while obfuscating circumstantial information. This paper addresses the question whether these findings can be transferred to process model maintenance. In particular, it investigates whether it is easier to incorporate sequential change requirements in imperative process models compared to circumstantial change requirements. To address this question this paper presents results from a controlled experiment providing evidence that the type of change (sequential versus circumstantial) has an effect on the accuracy of process models. For performance indicators modeling speed, correctness, and cognitive load no statistically significant differences could be identified.