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Requirement Specification Acquisition of Communications Services

Akira TAKURA, Yoshihiro UEDA, Tsuneki HAIZUKA, Tadashi OHTA

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Summary :

A requirement specification acquisition method combined with hypothesis-based reasoning and model reasoning is proposed for obtaining service specifications from the ambiguous and/or incomplete requirement specifications of communications services. Errors at an early stage of software development cost more to debug than those at a later stage. Specification acquisition is the most upstream development process. Nevertheless, the system support for specification acquisition is delayed compared with other development phases.' Users do not always have precise requirements. It is therefore inevitable that user requirements contain ambiguities, insufficiencies and even contradictions. Considering this, it is indispensable to support a specification completion method that derives service specifications from such problem requirements. This paper proposes a combined method to obtain consistent and complete specifications from such problem requirements. Communications service specifications can be described by specifying terminal behaviors which can be recognized from outside the communications system(s). Such specifications are described by a rule-based language. Requirement specifications usually have components that are ambiguous, incomplete, or even contradictory. They appear as rule description and/or missing rules. From such requirements, service specifications are obtained by using hypothesis-based reasoning on input requirements and existing service specifications. When existing specifications cannot be used to obtain complementary specifications, a communications service model is used to propose new rules. The proposed methods are implemented as a part of a communications software development system. The system enables non-experts in communications systems to define their own service specifications.

Publication
IEICE TRANSACTIONS on Information Vol.E79-D No.12 pp.1716-1725
Publication Date
1996/12/25
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Artificial Intelligence and Cognitive Science

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