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Static Analysis of BPMN-Based Process-Driven Applications

  • Process-Driven Applications (PDA) require less coding, for their business logic is defined by a business process model which can be executed by a process engine. However, inconsistencies between process model and dependent source code artifacts cause runtime errors and reduce development productivity. This paper targets at making the development of PDAs more efficient: It proposes a broader approach to statical analysis which also covers consistency constraints between model and code. When integrated into common analysis tools or a continuous integration pipeline, defects like broken code references or data-flow anomalies can be detected at an early stage without launching the entire application and its process interpretation engine. The approach is demonstrated by a prototype called viadee Process Application Validator (vPAV), which was developed for BPMN-based process models. The prototype has already been used in various BPM projects, attesting high benefit and potential.
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https://doi.org/10.1145/3297280.3297289

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Verfasserangaben:Konrad Schneid, Claus A. Usener, Sebastian Thöne, Herbert Kuchen, Christian Tophinke
URL:https://dl.acm.org/citation.cfm?doid=3297280.3297289
DOI:https://doi.org/10.1145/3297280.3297289
ISBN:978-1-4503-5933-7
Titel des übergeordneten Werkes (Englisch):Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
Verlag:ACM
Verlagsort:New York, NY, USA
Herausgeber:Chih-Cheng Hung, George A. Papadopoulos
Dokumentart:Beitrag in einer Konferenzveröffentlichung
Sprache:Englisch
Datum der Veröffentlichung (online):02.05.2019
Datum der Erstveröffentlichung:08.04.2019
Betreiber des Publikationsservers:FH Münster - University of Applied Sciences
Datum der Freischaltung:02.05.2019
Freies Schlagwort / Tag:BPM; BPMN; Business Process; Process-Driven Application; Static Analysis
Erste Seite:66
Letzte Seite:74
Fachbereiche:Wirtschaft (MSB)
Publikationsliste:Schneid, Konrad
Thöne, Sebastian
Lizenz (Deutsch):License LogoBibliographische Daten