Reference : Extracting Domain Models from Natural-Language Requirements: Approach and Industrial ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/28067
Extracting Domain Models from Natural-Language Requirements: Approach and Industrial Evaluation
English
Arora, Chetan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sabetzadeh, Mehrdad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zimmer, Frank []
Oct-2016
19th International Conference on Model Driven Engineering Languages and Systems, Saint-Malo 2-7 October 2016
Yes
19th International Conference on Model Driven Engineering Languages and Systems
October 2-7, 2016
Saint-Malo
France
[en] Model Extraction ; Natural-Language Requirements ; Natural Language Processing ; Case Study Research
[en] Domain modeling is an important step in the transition from natural-language requirements to precise specifications. For large systems, building a domain model manually is laborious. Several approaches exist to assist engineers with this task, where Natural Language Processing is employed for automated extraction of domain model elements. Despite the existing approaches, important facets remain under-explored. Notably, there is limited empirical evidence about the usefulness of existing extraction rules in industry. Furthermore, important opportunities for enhancing the extraction rules are yet to be exploited. We develop a domain model extractor by bringing together existing extraction rules and proposing important enhancements. We apply our model extractor to four industrial requirements documents, reporting on the frequency of different extraction rules being applied. We conduct an expert study over one of these documents, investigating the accuracy and overall effectiveness of our domain model extractor.
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/28067
FnR ; FNR6911386 > Chetan Arora > > Enhancing the Automation and Accuracy of Requirements Quality Assurance Processes via Disciplined Use of Natural Language > 01/09/2013 > 31/10/2016 > 2013

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