Automated Analysis of Natural Language Properties for UML Models

It is well known that errors introduced early in the development process are commonly the most expensive to correct. The increasingly popular model-driven architecture (MDA) exacerbates this problem by propagating these errors automatically to design and code. This paper describes a round trip engin...

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Bibliographic Details
Published inLecture notes in computer science pp. 48 - 57
Main Authors Konrad, Sascha, Cheng, Betty H. C.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Case Tool
Computer science; control theory; systems
Exact sciences and technology
Natural Language
Object Management Group
Pattern System
Property Deriver
Software
Software engineering
Speech and sound recognition and synthesis. Linguistics
Language analysis
Software architecture
Model matching
Software development
Error estimation
Behavioral analysis
Model specification
Model driven architecture
Unified modelling language
Natural language
Behavior model
Modeling
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Summary:It is well known that errors introduced early in the development process are commonly the most expensive to correct. The increasingly popular model-driven architecture (MDA) exacerbates this problem by propagating these errors automatically to design and code. This paper describes a round trip engineering process that supports the specification of a UML model using CASE tools, the analysis of specified natural language properties, and the subsequent model refinement to eliminate errors uncovered during the analysis. This process has been implemented in Spider, a tool suite that enables developers to specify and analyze a UML model with respect to behavioral properties specified in terms of natural language.
Bibliography:This work has been supported in part by NSF grants EIA-0000433, EIA-0130724, CDA-9700732, CCR-9901017, Department of the Navy, Office of Naval Research under Grant No. N00014-01-1-0744, Eaton Corporation, Siemens Corporate Research, and in cooperation with Siemens Automotive, Detroit Diesel Corporation, and General Dynamics Land Systems.
ISBN:9783540317807
3540317805
ISSN:0302-9743
1611-3349
DOI:10.1007/11663430_6