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Natural language ambiguity resolution by intelligent semantic annotation of software requirements

Natural language ambiguity resolution by intelligent semantic annotation of software requirements Natural Language (NL) is the root cause of ambiguity in the SRS document. The quality of the software development process can be improved by mitigating the risk with the use of semantically controlled representation. A possible solution to handle ambiguity can be the use of a mathematical formal logic representation in place of NL to capture software requirements. However, the use of formal logic is a complex task. A wrongly written formal logic will be difficult to handle and it will create serious problems in later stages of software development. Furthermore, stakeholders are typically not able to understand mathematical logic. Hence, this solution does not look feasible. Another possible way of addressing above discussed ambiguity problem is the use of controlled natural languages (CNL). It can work as a bridge between NL and formal representation. Since Requirement Analysis is based on communication and the analyst’s experience, it can be modeled up to a certain limit. This limit gives birth to controlled language. If the document is written in a controlled language, it will be feasible for the development team to use a simpler and less costly linguistic tool. The CNLs are syntactically unambiguous, semantically consistent and, controlled. Several CNLs could be found in literature such as ACE, PENG, CPL, Formalized-English, and Semantics of Business Vocabulary and Rules (SBVR), etc. We aim to use an SBVR based CNL to capture stakeholder’s requirements and prepare an SRS document using SBVR. Such software requirements will not only be syntactically clear but also semantically consistent. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automated Software Engineering Springer Journals

Natural language ambiguity resolution by intelligent semantic annotation of software requirements

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References (59)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
ISSN
0928-8910
eISSN
1573-7535
DOI
10.1007/s10515-021-00291-0
Publisher site
See Article on Publisher Site

Abstract

Natural Language (NL) is the root cause of ambiguity in the SRS document. The quality of the software development process can be improved by mitigating the risk with the use of semantically controlled representation. A possible solution to handle ambiguity can be the use of a mathematical formal logic representation in place of NL to capture software requirements. However, the use of formal logic is a complex task. A wrongly written formal logic will be difficult to handle and it will create serious problems in later stages of software development. Furthermore, stakeholders are typically not able to understand mathematical logic. Hence, this solution does not look feasible. Another possible way of addressing above discussed ambiguity problem is the use of controlled natural languages (CNL). It can work as a bridge between NL and formal representation. Since Requirement Analysis is based on communication and the analyst’s experience, it can be modeled up to a certain limit. This limit gives birth to controlled language. If the document is written in a controlled language, it will be feasible for the development team to use a simpler and less costly linguistic tool. The CNLs are syntactically unambiguous, semantically consistent and, controlled. Several CNLs could be found in literature such as ACE, PENG, CPL, Formalized-English, and Semantics of Business Vocabulary and Rules (SBVR), etc. We aim to use an SBVR based CNL to capture stakeholder’s requirements and prepare an SRS document using SBVR. Such software requirements will not only be syntactically clear but also semantically consistent.

Journal

Automated Software Engineering – Springer Journals

Published: Jul 21, 2021

Keywords: Software requirements; Ambiguity Resolution; Semantic annotation; SBVR

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