Comprehension and recall of miniature programs
International Journal of Man-Machine Studies - Lecture notes in computer science Vol. 174
Object-oriented metrics: measures of complexity
Object-oriented metrics: measures of complexity
A study in process simplification
ICSP '96 Proceedings of the Fourth International Conference on the Software Process (ICSP '96)
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Data & Knowledge Engineering - Special issue: Quality in conceptual modeling
Evaluation measures for business process models
Proceedings of the 2006 ACM symposium on Applied computing
Process control-flow complexity metric: An empirical validation
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Conformance checking of processes based on monitoring real behavior
Information Systems
Evaluating workflow process designs using cohesion and coupling metrics
Computers in Industry
On a Quest for Good Process Models: The Cross-Connectivity Metric
CAiSE '08 Proceedings of the 20th international conference on Advanced Information Systems Engineering
An Ensemble of Complexity Metrics for BPEL Web Processes
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Measuring Entropy in Business Process Models
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness
Complexity metrics for Workflow nets
Information and Software Technology
Prediction Models for BPMN Usability and Maintainability
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Business Process Modeling: Perceived Benefits
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
Finding software metrics threshold values using ROC curves
Journal of Software Maintenance and Evolution: Research and Practice
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
Business process quality metrics: log-based complexity of workflow patterns
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
What makes process models understandable?
BPM'07 Proceedings of the 5th international conference on Business process management
Quality indicators for business process models from a gateway complexity perspective
Information and Software Technology
Understanding business process models: the costs and benefits of structuredness
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Assessing the best-order for business process model refactoring
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Process improvement is recognized as the main benefit of process modelling initiatives. Quality considerations are important when conducting a process modelling project. While the early stage of business process design might not be the most expensive ones, they tend to have the highest impact on the benefits and costs of the implemented business processes. In this context, quality assurance of the models has become a significant objective. In particular, understandability and modifiability are quality attributes of special interest in order to facilitate the evolution of business models in a highly dynamic environment. These attributes can only be assessed a posteriori, so it is of central importance for quality management to identify significant predictors for them. A variety of structural metrics have recently been proposed, which are tailored to approximate these usage characteristics. The aim of this paper is to verify how understandable and modifiable BPMN models relate to these metrics by means of correlation and regression analyses. Based on the results we determine threshold values to distinguish different levels of process model quality. As such threshold values are missing in prior research, we expect to see strong implications of our approach on the design of modelling guidelines.