Masterarbeit MSTR-2017-05

Al-Hashimi, Ali: Data Generator for BPMN 2.0 Models Designed for Performance Testing.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 5 (2017).
88 Seiten, englisch.

Recently more and more organizations have been using business process models to improve the efficiency, to optimize their businesses and to validate them. Currently, most business analysts use Business Process Modeling and Notation (BPMN 2.0) as a modeling language to design their business process models, since it can be understood by non-technical persons. However, the conceptual BPMN 2.0 process models can not be executed by workflow engines without modifying or translating these models to executable models. For this reason, translating conceptual business processes to executable ones has gained massive attention. To surmount this limitation, this thesis proposes a prototype Data Generator Application responsible for converting conceptual BPMN 2.0 process models to executable models. Our idea, to produce executable business process models, is different from the methods in the literature since we do not translate the source model to other kinds of models such as Business Process Execution Language (BPEL) or executable code. In this thesis, we have focused on producing different types of data that are needed by various BPMN 2.0 elements. These elements are available in most conceptual models, and most core BPMN 2.0 elements are covered by our approach. After generating these data types, the modified (executable) model is retained as BPMN 2.0 file. The modified BPMN file resulting from our purposed Data Generator,Web-based Application, can be executed by two workflow engines. Moreover, dynamic deployment and execution services for the models resulted from our application is also considered in the second part of the application.

Abteilung(en)Universität Stuttgart, Institut für Architektur von Anwendungssystemen
BetreuerLeymann, Prof. Frank; Skouradaki, Marigianna
Eingabedatum28. Mai 2019
   Publ. Institut   Publ. Informatik