Translating natural language constraints to OCL
Author(s) -
Imran Sarwar Bajwa,
Mark Lee,
Behzad Bordbar
Publication year - 2012
Publication title -
journal of king saud university - computer and information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 33
eISSN - 2213-1248
pISSN - 1319-1578
DOI - 10.1016/j.jksuci.2011.12.003
Subject(s) - object constraint language , computer science , semantics of business vocabulary and business rules , programming language , unified modeling language , natural language processing , semantics (computer science) , natural language , abstract syntax , syntax , artificial intelligence , object language , model transformation , software , business rule , applications of uml , business process , work in process , marketing , business , consistency (knowledge bases)
Object Constraint Language (OCL) is the only available language to annotate the Unified Modeling Language (UML) based conceptual schema (CS) of a software application. In practice, the constraints are captured in a natural language (NL) such as English and then an OCL expert manually transforms the NL expressions to OCL syntax. However, it is a common knowledge that OCL is difficult to write specifically for the novel users with little or no prior knowledge of OCL. In recent times, model transformation technology has made transformation of one language to another simpler and easier. In this paper we present a novel approach to automatically transform NL specification of software constraints to OCL constraints. In NL to OCL transformation, Semantics of Business Vocabulary and Rules (SBVR) standard is used as an intermediate representation due to a couple of reasons: first of all, SBVR is based on higher order logic that simplifies the transformation of SBVR to other formal languages such as OCL. Moreover, SBVR used syntax of natural language and thus is close to human beings. The presented NL to OCL transformation via SBVR will not only simplify the process of generating OCL constraints but also generate accurate models in less time
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