| Reference : Automated Extraction and Clustering of Requirements Glossary Terms |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| Computational Sciences | |||
| http://hdl.handle.net/10993/28943 | |||
| Automated Extraction and Clustering of Requirements Glossary Terms | |
| English | |
Arora, Chetan  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Sabetzadeh, Mehrdad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Zimmer, Frank [SES Techcom, Luxembourg] | |
| Oct-2017 | |
| IEEE Transactions on Software Engineering | |
| Institute of Electrical and Electronics Engineers | |
| 43 | |
| 10 | |
| 918-945 | |
| Yes (verified by ORBilu) | |
| 0098-5589 | |
| New York | |
| NY | |
| [en] Requirements Glossaries ; Term Extraction ; Natural Language Processing ; Clustering ; Case Study Research | |
| [en] A glossary is an important part of any software requirements document. By making explicit the technical terms in a domain and providing definitions for them, a glossary helps mitigate imprecision and ambiguity. A key step in building a glossary is to decide upon the terms to include in the glossary and to find any related terms. Doing so manually is laborious, particularly for large requirements documents.
In this article, we develop an automated approach for extracting candidate glossary terms and their related terms from natural language requirements documents. Our approach differs from existing work on term extraction mainly in that it clusters the extracted terms by relevance, instead of providing a flat list of terms. We provide an automated, mathematically-based procedure for selecting the number of clusters. This procedure makes the underlying clustering algorithm transparent to users, thus alleviating the need for any user-specified parameters. To evaluate our approach, we report on three industrial case studies, as part of which we also examine the perceptions of the involved subject matter experts about the usefulness of our approach. Our evaluation notably suggests that: (1) Over requirements documents, our approach is more accurate than major generic term extraction tools. Specifically, in our case studies, our approach leads to gains of 20% or more in terms of recall when compared to existing tools, while at the same time either improving precision or leaving it virtually unchanged. And, (2) the experts involved in our case studies find the clusters generated by our approach useful as an aid for glossary construction. | |
| Interdisciplinary Centre for Security, Reliability and Trust - SnT | |
| Fonds National de la Recherche - FnR | |
| http://hdl.handle.net/10993/28943 | |
| 10.1109/TSE.2016.2635134 | |
| H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems | |
| 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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