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Early Identification of Implicit Requirements with the COTIR Approach using Common Sense, Ontology and Text Mining

Emebo, Onyeka and Varde, Aparna S. (2016) Early Identification of Implicit Requirements with the COTIR Approach using Common Sense, Ontology and Text Mining. Technical Report. MONTCLAIR STATE UNIVERSITY MONTCLAIR, NJ, USA.

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The ability of a system to meet its requirements is a strong determinant of success. Thus effective Software Requirements Specification (SRS) is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this research, we emphasize that commonsense knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This research is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach called COTIR (Commonsense, Ontology and Text mining for Implicit Requirements) to identify and manage IMRs. As the name implies, COTIR is based on an integrated framework of three core technologies: commonsense knowledge (CSK), text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development.

Item Type: Monograph (Technical Report)
Uncontrolled Keywords: Commonsense Knowledge, Implicit Requirements, Ontology, Requirements Engineering, Text Mining
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 14 Mar 2019 08:46
Last Modified: 14 Mar 2019 08:46

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