Identifying Error-Prone Software An Empirical Study
IEEE Transactions on Software Engineering
An empirical study of software design practices
IEEE Transactions on Software Engineering
Analyzing Error-Prone System Structure
IEEE Transactions on Software Engineering
ACM '85 Proceedings of the 1985 ACM annual conference on The range of computing : mid-80's perspective: mid-80's perspective
A quantitative framework for software restructuring
Journal of Software Maintenance: Research and Practice
Sap R/3 Process Oriented Implementation
Sap R/3 Process Oriented Implementation
Measuring Design-Level Cohesion
IEEE Transactions on Software Engineering
Distributed and Parallel Databases
Guidelines of Business Process Modeling
Business Process Management, Models, Techniques, and Empirical Studies
Measuring Attributes of Concurrent Software Specifications in Petri Nets
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Process models representing knowledge for action: a revised quality framework
European Journal of Information Systems - Special issue: Action in language, organisations and information systems
Process control-flow complexity metric: An empirical validation
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Detection and prediction of errors in EPCs of the SAP reference model
Data & Knowledge Engineering
Formalization and verification of EPCs with OR-joins based on state and context
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
Understanding the occurrence of errors in process models based on metrics
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
A discourse on complexity of process models
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
Quality metrics for business process modeling
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
An entropy-based uncertainty measure of process models
Information Processing Letters
Quality assessment of business process models based on thresholds
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Survey paper: Refactoring large process model repositories
Computers in Industry
Towards thresholds of control flow complexity measures for BPMN models
Proceedings of the 2011 ACM Symposium on Applied Computing
Syntax highlighting in business process models
Decision Support Systems
A semantic approach for business process model abstraction
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Thresholds for error probability measures of business process models
Journal of Systems and Software
Understanding business process models: the costs and benefits of structuredness
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Understanding understandability of conceptual models --- what are we actually talking about?
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Journal of Database Management
Hi-index | 0.00 |
Business process modeling is an important corporate activity, but the understanding of what constitutes good process models is rather limited. In this paper, we turn to the cognitive dimensions framework and identify the understanding of the structural relationship between any pair of model elements as a hard mental operation. Based on the weakest-link metaphor, we introduce the cross-connectivity metric that measures the strength of the links between process model elements. The definition of this new metric builds on the hypothesis that process models are easier understood and contain less errors if they have a high cross-connectivity. We undertake a thorough empirical evaluation to test this hypothesis and present our findings. The good performance of this novel metric underlines the importance of cognitive research for advancing the field of process model measurement.