Prof Sunil Vadera S.Vadera@salford.ac.uk
Professor
From English to formal specifications
Vadera, S; Meziane, F
Authors
F Meziane
Abstract
Formal methods provide an approach in which design steps can be shown to satisfy a specification. However, if a formal specification is wrong, then although the design steps may satisfy the formal specification, they are unlikely to satisfy the requirements of the system. Since most users are unfamiliar with formal methods, requirements specifications are often written in English. Such requirements, expressed in English, are then somehow translated to formal specifications. This transition has some potential for introducing errors and inconsistencies.
In this paper we propose an interactive approach to proceeding from an informal specification to a formal
specification in a systematic manner. The approach uses research in the area of natural language understanding to analyse English specifications in order to detect ambiguities and to generate an entity relationship model. The entity relationship model is then used as a basis for producing VDM data types and the specifications of some common operations.
We illustrate the effectiveness of our approach by applying it to the specification of part of a route planning database system.
Citation
Vadera, S., & Meziane, F. (1994). From English to formal specifications. Computer Journal, 37(9), 753-763. https://doi.org/10.1093/comjnl/37.9.753
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 1994 |
Deposit Date | Jan 16, 2009 |
Publicly Available Date | Apr 5, 2016 |
Journal | Computer Journal |
Print ISSN | 0010-4620 |
Publisher | Oxford University Press |
Peer Reviewed | Peer Reviewed |
Volume | 37 |
Issue | 9 |
Pages | 753-763 |
DOI | https://doi.org/10.1093/comjnl/37.9.753 |
Publisher URL | http://dx.doi.org/10.1093/comjnl/37.9.753 |
Files
Accepted Version
(2.8 Mb)
PDF
You might also like
Development of an evolutionary cost sensitive decision tree induction algorithm
(2022)
Presentation / Conference
Phishing website detection from URLs using classical machine learning ANN model
(2021)
Journal Article
Cost-sensitive meta-learning framework
(2021)
Journal Article
Phishing email detection using Natural Language Processing techniques : a literature survey
(2021)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search