Business process models play an important role for the management, design, and improvement of process organizations and process-aware information systems. Despite the extensive application of process modeling in practice, there are hardly empirical results available on quality aspects of process models. This paper aims to advance the understanding of this matter by analyzing the connection between formal errors (such as deadlocks) and a set of metrics that capture various structural and behavioral aspects of a process model. In particular, we discuss the theoretical connection between errors and metrics, and provide a comprehensive validation based on an extensive sample of EPC process models from practice. Furthermore, we investigate the capability of the metrics to predict errors in a second independent sample of models. The high explanatory power of the metrics has considerable consequences for the design of future modeling guidelines and modeling tools.

Understanding the Occurrence of Errors in Process Models Based on Metrics

Business process models expressed in languages such as BPMN (Business Process Model and Notation) play a critical role in implementing the workflows in modern organizations. However, control flow errors such as deadlock and lack of synchronization as well as syntactic errors arising out of poor modeling practices often occur in industrial process models. In this paper, we provide an empirical diagnostic analysis of such errors for real-life industrial process models. The investigation involved models from different application domains. It turns out that error frequency has non-linear relation with error depth (the maximum depth at which an error occurred) across models from all domains. Error occurrence has statistically significant correlations (p

A. Gopichand

Papers

An Empirical Analysis of Diagnosis of Industrial Business Processes at Sub-process Levels