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

The Business Process Management Lifecycle

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)

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

Example EPC process model

EPC Functions and Events

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

EPC Behavior d w

Problems with Connector Mismatch

EPC Soundness Start and End Events

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

Reduction Rules

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

Formal Errors

Verification Performance for SAP Ref.-Mod.

Verification Performance for SAP Ref.-Mod. II

Verification Performance for SAP Ref.-Mod. III

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

Model Metrics: Connector Mismatch (MM) = 14

Model Metrics: Cyclicity (CYC) = 0

Model Metrics: Separability (Pi) = 0.455

Model Metrics: Structuredness (Phi) = 0.652

Model Metrics: Connector Heterogeneity (CH) = 0.873

Model Metrics: Diameter 1 2 3 = 14 4 5 6 7 8 9 10 11 12 13 14

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

Logistic Regression Results

Results

Error Prediction = 0.811 > 0.5

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

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

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