Detecting Data-State Anomalies in BPMN 2.0
| Vortragende(r) | Pierre Bonert | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Jutta Mülle | |
| Termin | Fr 17. Januar 2020 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Business Process Model and Notation (BPMN) is a standard language to specify business
process models. It helps organizations around the world to analyze, improve and automate their processes. It is very important to make sure that those models are correct, as faulty models can do more harm than good. While many verification methods for BPMN concentrate only on control �ow, the importance of correct data �ow is often neglected. Additionally the few approaches tackling this problem, only do it on a surface level ignoring certain important aspects, such as data states. Because data objects with states can cause different types of errors than data objects without them, ignoring data states can lead to overlooking certain mistakes. This thesis tries to address the problem of detecting data �ow errors on the level of data states, while also taking optional data and alternative data into account. We propose a new transformation for BPMN models to Petri Nets and specify suitable anti-patterns. Using a model checker, we are then capable of automatically detecting data �ow errors regarding data states. In combination with existing approaches, which detect control �ow errors or data �ow errors on the level of data values, business process designers will be able to prove with a higher certainty that their models are actually �flawless. | |