Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization

Emebo Onyeka; Aparna S. Varde; Vaibhav Anu; Niket Tandon; Olawande Daramola

doi:10.1109/ICTAI50040.2020.00146

Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization

Emebo Onyeka
, Aparna S. Varde
, Vaibhav Anu
, Niket Tandon
, Olawande Daramola

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
Editors	Miltos Alamaniotis, Shimei Pan
Publisher	IEEE Computer Society
Pages	935-940
Number of pages	6
ISBN (Electronic)	9781728192284
DOIs	https://doi.org/10.1109/ICTAI50040.2020.00146
State	Published - Nov 2020
Event	32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 - Virtual, Baltimore, United States Duration: 9 Nov 2020 → 11 Nov 2020

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	2020-November
ISSN (Print)	1082-3409

Conference

Conference	32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
Country/Territory	United States
City	Virtual, Baltimore
Period	9/11/20 → 11/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

AI in Smart Cities
Commonsense Knowledge
Implicit Requirements
Ontology
Requirements Engineering
Software Demo
Text Mining

Access to Document

10.1109/ICTAI50040.2020.00146

Cite this

Onyeka, E., Varde, A. S., Anu, V., Tandon, N., & Daramola, O. (2020). Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization. In M. Alamaniotis, & S. Pan (Eds.), Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020 (pp. 935-940). Article 9288192 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/ICTAI50040.2020.00146

Onyeka, Emebo ; Varde, Aparna S. ; Anu, Vaibhav et al. / Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization. Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020. editor / Miltos Alamaniotis ; Shimei Pan. IEEE Computer Society, 2020. pp. 935-940 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI).

@inproceedings{59dcfed92e524e9fae7f80fe5c85cd6f,

title = "Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization",

abstract = "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.",

keywords = "AI in Smart Cities, Commonsense Knowledge, Implicit Requirements, Ontology, Requirements Engineering, Software Demo, Text Mining",

author = "Emebo Onyeka and Varde, \{Aparna S.\} and Vaibhav Anu and Niket Tandon and Olawande Daramola",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 ; Conference date: 09-11-2020 Through 11-11-2020",

year = "2020",

month = nov,

doi = "10.1109/ICTAI50040.2020.00146",

language = "English",

series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",

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}

Onyeka, E, Varde, AS , Anu, V, Tandon, N & Daramola, O 2020, Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization. in M Alamaniotis & S Pan (eds), Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020., 9288192, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2020-November, IEEE Computer Society, pp. 935-940, 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020, Virtual, Baltimore, United States, 9/11/20. https://doi.org/10.1109/ICTAI50040.2020.00146

Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization. / Onyeka, Emebo; Varde, Aparna S.; Anu, Vaibhav et al.
Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020. ed. / Miltos Alamaniotis; Shimei Pan. IEEE Computer Society, 2020. p. 935-940 9288192 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2020-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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.

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KW - Implicit Requirements

KW - Ontology

KW - Requirements Engineering

KW - Software Demo

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M3 - Conference contribution

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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 -

Onyeka E, Varde AS , Anu V, Tandon N, Daramola O. Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization. In Alamaniotis M, Pan S, editors, Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020. IEEE Computer Society. 2020. p. 935-940. 9288192. (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). doi: 10.1109/ICTAI50040.2020.00146

Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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