1. Detection and Prediction of Errors in EPC Business Process Models
Jan Mendling
WU Wien
Austria

2. The Business Process Management Lifecycle

3. Business Process Model Quality and Errors
The need for appropriately qualified process modelers increases with the size of the initiative as it becomes important that adequate quality assurance procedures are inplace. It is not possible to control the different quality aspects after the models are designed, if on a single day 100+ hours are spent on designing new models. (Rosemann 2006)
The cost of errors increases exponentially over the development lifecycle: it is more than 100 times more costly to correct a defect post-implementation than it is to correct it during requirements analysis. (Boehm 1981, Moody 2005)

4. Agenda EPC Business Process Models Verification of EPC Soundness
Metrics for Business Process Models
Prediction of Errors based on Metrics

5. Example EPC process model

6. EPC Functions and Events

7. EPC Connectors
AND-Join
AND-Split
XOR-Split
OR-Split
OR-Join
XOR-Join

8. EPC Behavior d w

9. Problems with Connector Mismatch

10. EPC Soundness
Start and End Events

11. Reachability Graph Calculation
State Explosion Problem:
348 = 79,766,443,076,872,509,863,361 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 41 43 47 34 27 28 19 35 42 44 48 20 37 21 45 46 38 29 36 24 22 39 26 25 30 40 31 32 33

12. Reduction Rules

13. Tool Support for Soundness Verification
Reduction Rules
with xoEPC
Reachability Graph
Calculation with ProM

14. Formal Errors

15. Verification Performance for SAP Ref.-Mod.

16. Verification Performance for SAP Ref.-Mod. II

17. Verification Performance for SAP Ref.-Mod. III

18. Model Metrics: Coefficient of Network Connectivity (CNC)
= 1.043

19. Model Metrics: Connector Mismatch (MM)
= 14

20. Model Metrics: Cyclicity (CYC)
= 0

21. Model Metrics: Separability (Pi)
= 0.455

22. Model Metrics: Structuredness (Phi)
= 0.652

23. Model Metrics: Connector Heterogeneity (CH)
= 0.873

24. Model Metrics: Diameter1 2 3
= 14 4 5 6 7 8 9 10 11 12 13 14

25. Metrics and Errors for a Sample of 2003 EPCs
rPhi,hasError= -0,36
rCH,hasError= 0,46

26. Logistic Regression Results

27. Results

28. Error Prediction
= > 0.5

29. Holdout Sample Results
113 EPCs from books by
Scheer,
Becker & Schütte,
Staud, and
Seidlmeier
Accuracy interval for prediction function
81.15% to 96.77%
with 95% confidence

30. Contributions Formalization of the OR-Join
Verification of Process Models with OR-Joins and multiple Start- and End-Events
Metrics for Business Process Models
Validation of Metrics as Error Predictors

31. Discussion Importance of Verification
Business Process Modeling Process
Business Process Modeling Tools
Teaching of Process Modeling