An analysis of ambiguity detection techniques for Software Requirements Specification (SRS)
Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are writ-ten in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based...
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iium-644632019-03-22T08:07:40Z http://irep.iium.edu.my/64463/ An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) Oo, Khin Hayman Nordin, Azlin Ismail, Amelia Ritahani Sulaiman, Suriani QA76 Computer software Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are writ-ten in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS. Science Publishing Corporation 2018-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64463/1/An%20Analysis%20of%20Ambiguity%20Detection%20Techniques%20for%20SRS.pdf Oo, Khin Hayman and Nordin, Azlin and Ismail, Amelia Ritahani and Sulaiman, Suriani (2018) An analysis of ambiguity detection techniques for Software Requirements Specification (SRS). International Journal of Engineering & Technology, 7 (2.29 (Special Issue 29)). pp. 501-505. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/13808/5517 10.14419/ijet.v7i2.29.13808Published on: 22-05-2018 |
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QA76 Computer software Oo, Khin Hayman Nordin, Azlin Ismail, Amelia Ritahani Sulaiman, Suriani An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
description |
Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are writ-ten in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS. |
format |
Article |
author |
Oo, Khin Hayman Nordin, Azlin Ismail, Amelia Ritahani Sulaiman, Suriani |
author_facet |
Oo, Khin Hayman Nordin, Azlin Ismail, Amelia Ritahani Sulaiman, Suriani |
author_sort |
Oo, Khin Hayman |
title |
An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
title_short |
An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
title_full |
An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
title_fullStr |
An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
title_full_unstemmed |
An analysis of ambiguity detection techniques for Software Requirements Specification (SRS) |
title_sort |
analysis of ambiguity detection techniques for software requirements specification (srs) |
publisher |
Science Publishing Corporation |
publishDate |
2018 |
url |
http://irep.iium.edu.my/64463/ http://irep.iium.edu.my/64463/ http://irep.iium.edu.my/64463/ http://irep.iium.edu.my/64463/1/An%20Analysis%20of%20Ambiguity%20Detection%20Techniques%20for%20SRS.pdf |
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2023-09-18T21:31:29Z |
last_indexed |
2023-09-18T21:31:29Z |
_version_ |
1777412540184133632 |