Automatically mapping a requirement
specification to design model in Software Engineering is an open complex problem.
Existing methods use a complex manual process that use the knowledge from the
requirement specification/modeling and the design, and try to find a good match
between them. The key task done by designers is to convert a natural language
based requirement specification (or corresponding UML based representation)
into a predominantly computer language based design model—thus
the process is very complex as there is a very large gap between our natural
language and computer language. Moreover, this is not just a simple language
conversion, but rather a complex knowledge conversion that can lead to meaningful design
implementation. In this paper, we describe an
automated method to map Requirement Model to Design Model and thus automate/partially
automate the Structured Design (SD) process. We believe, this is the first
logical step in mapping a more complex requirement specification to design
model. We call it IRTDM (Intelligent Agent based requirement model to design
model mapping). The main theme of IRTDM is to use some AI (Artificial
Intelligence) based algorithms, semantic representation using Ontology or
Predicate Logic, design structures using some well known design framework and
Machine Learning algorithms for learning over time. Semantics help convert
natural language based requirement specification (and associated UML
representation) into high level design model followed by mapping to design
structures. AI method can also be used to convert high level design structures
into lower level design which then can be refined further by some manual and/or
semi automated process. We emphasize that automation is one of the key ways
to minimize the software cost, and is very important for all, especially, for the “Design
for the Bottom 90% People” or BOP (Base of the Pyramid People).
Cite this paper
E. Khan and M. Alawairdhi, "Intelligent Agent Based Mapping of Software Requirement Specification to Design Model," Journal of Software Engineering and Applications
, Vol. 6 No. 12, 2013, pp. 630-637. doi: 10.4236/jsea.2013.612075
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