Transformation of SBVR business rules to UML class model

Stuti Awasthi, Ashalatha Nayak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Multiple attempts have been made these days to automate the creation of class diagrams by providing structured English statements as input. The resulting diagrams are of close proximity to what the user wants. This paper is one such attempt to transform business designs written in OMG's (Object Management Group) standard SBVR (Semantics of Business Vocabulary and Rules) framework, into a set of classes in UML (Unified Modeling Language) Class Model using a theoretical approach. SBVR provides a set of specific rules which are processed in order to get class diagram of close proximity. It involves the transformation of "Structured English" into a set of UML Class Model with SBVR as a mediator. Further, the results of the approach are validated using VeTIS tool.

Original languageEnglish
Title of host publicationParameterized and Exact Computation - 7th International Symposium, IPEC 2012, Proceedings
Pages277-288
Number of pages12
DOIs
Publication statusPublished - 02-12-2013
Event7th International Symposium on Parameterized and Exact Computation, IPEC 2012 - Ljubljana, Slovenia
Duration: 12-09-201314-09-2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7735 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Symposium on Parameterized and Exact Computation, IPEC 2012
CountrySlovenia
CityLjubljana
Period12-09-1314-09-13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Awasthi, S., & Nayak, A. (2013). Transformation of SBVR business rules to UML class model. In Parameterized and Exact Computation - 7th International Symposium, IPEC 2012, Proceedings (pp. 277-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7735 LNCS). https://doi.org/10.1007/978-3-642-35786-2_21