Detecting Infeasible Traces in Process Models

Zhaoxia Wang, Lijie Wen, Xiaochen Zhu, Yingbo Liu, Jianmin Wang

2012

Abstract

Workflow testing is an important method of workflow analysis in design time. A challenging problem with trace-oriented test data generation in particular and trace-based workflow analysis in general is the existence of infeasible traces for which there is no input data for them to be executed. In this paper we build on the theory of workflow nets and introduce workflow nets where transitions have conditions associated with them. We then demonstrate that we can determine which execution traces, that are possible according to the controlflow dependencies, are actually possible taking the data perspective into account. This way we are able to more accurately determine in design time the infeasible traces caused by the correlation between transition conditions along this trace. Finally, we provide a solution to automatically detecting the shortest infeasible trace.

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Paper Citation


in Harvard Style

Wang Z., Wen L., Zhu X., Liu Y. and Wang J. (2012). Detecting Infeasible Traces in Process Models . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-8565-12-9, pages 212-217. DOI: 10.5220/0003989002120217

in Bibtex Style

@conference{iceis12,
author={Zhaoxia Wang and Lijie Wen and Xiaochen Zhu and Yingbo Liu and Jianmin Wang},
title={Detecting Infeasible Traces in Process Models},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2012},
pages={212-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003989002120217},
isbn={978-989-8565-12-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Detecting Infeasible Traces in Process Models
SN - 978-989-8565-12-9
AU - Wang Z.
AU - Wen L.
AU - Zhu X.
AU - Liu Y.
AU - Wang J.
PY - 2012
SP - 212
EP - 217
DO - 10.5220/0003989002120217