TY - GEN
T1 - Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization
AU - Onyeka, Emebo
AU - Varde, Aparna S.
AU - Anu, Vaibhav
AU - Tandon, Niket
AU - Daramola, Olawande
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo paper would be useful to Software Engineers since it deals with automation in the requirements analysis phase, thus contributing to Requirements Engineering. It would interest AI scientists as it entails multi-disciplinary work encompassing text mining, ontology and commonsense knowledge. It makes a broader impact on Smart Cities, because automated identification of IMRs would offer inputs to Smart City Tools, where requirements may often be implicit given that Smart Cities are an emerging and growing paradigm.
AB - This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo paper would be useful to Software Engineers since it deals with automation in the requirements analysis phase, thus contributing to Requirements Engineering. It would interest AI scientists as it entails multi-disciplinary work encompassing text mining, ontology and commonsense knowledge. It makes a broader impact on Smart Cities, because automated identification of IMRs would offer inputs to Smart City Tools, where requirements may often be implicit given that Smart Cities are an emerging and growing paradigm.
KW - AI in Smart Cities
KW - Commonsense Knowledge
KW - Implicit Requirements
KW - Ontology
KW - Requirements Engineering
KW - Software Demo
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85098785817&partnerID=8YFLogxK
U2 - 10.1109/ICTAI50040.2020.00146
DO - 10.1109/ICTAI50040.2020.00146
M3 - Conference contribution
AN - SCOPUS:85098785817
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 935
EP - 940
BT - Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
A2 - Alamaniotis, Miltos
A2 - Pan, Shimei
PB - IEEE Computer Society
T2 - 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
Y2 - 9 November 2020 through 11 November 2020
ER -
