research-article

A methodology for the classification of quality of requirements using machine learning techniques

Authors:

Christos Dimou,

Valentín Moreno,

Anabel FragaAuthors Info & Claims

Information and Software Technology, Volume 67, Issue C

Pages 180 - 195

https://doi.org/10.1016/j.infsof.2015.07.006

Published: 01 November 2015 Publication History

Abstract

ContextOne of the most important factors in the development of a software project is the quality of their requirements. Erroneous requirements, if not detected early, may cause many serious problems, such as substantial additional costs, failure to meet the expected objectives and delays in delivery dates. For these reasons, great effort must be devoted in requirements engineering to ensure that the project's requirements results are of high quality. One of the aims of this discipline is the automatic processing of requirements for assessing their quality; this aim, however, results in a complex task because the quality of requirements depends mostly on the interpretation of experts and the necessities and demands of the project at hand. ObjectiveThe objective of this paper is to assess the quality of requirements automatically, emulating the assessment that a quality expert of a project would assess. MethodThe proposed methodology is based on the idea of learning based on standard metrics that represent the characteristics that an expert takes into consideration when deciding on the good or bad quality of requirements. Using machine learning techniques, a classifier is trained with requirements earlier classified by the expert, which then is used for classifying newly provided requirements. ResultsWe present two approaches to represent the methodology with two situations of the problem in function of the requirement corpus learning balancing, obtaining different results in the accuracy and the efficiency in order to evaluate both representations. The paper demonstrates the reliability of the methodology by presenting a case study with requirements provided by the Requirements Working Group of the INCOSE organization. ConclusionsA methodology that evaluates the quality of requirements written in natural language is presented in order to emulate the quality that the expert would provide for new requirements, with 86.1 of average in the accuracy.

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Cited By

Hassan SLi QAurangzeb KYasin AKhan JAnwar M(2024)A systematic mapping to investigate the application of machine learning techniques in requirement engineering activitiesCAAI Transactions on Intelligence Technology10.1049/cit2.123489:6(1412-1434)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1049/cit2.12348
Frattini JFucci DTorkar RMontgomery LUnterkalmsteiner MFischbach JMendez D(2024)Applying bayesian data analysis for causal inference about requirements quality: a controlled experimentEmpirical Software Engineering10.1007/s10664-024-10582-130:1Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1007/s10664-024-10582-1
Umar MLano K(2024)Advances in automated support for requirements engineering: a systematic literature reviewRequirements Engineering10.1007/s00766-023-00411-029:2(177-207)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00766-023-00411-0
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A methodology for the classification of quality of requirements using machine learning techniques
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cover image Information and Software Technology

Information and Software Technology Volume 67, Issue C

November 2015

265 pages

ISSN:0950-5849

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Copyright © Elsevier B.V.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 November 2015

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Cited By

Hassan SLi QAurangzeb KYasin AKhan JAnwar M(2024)A systematic mapping to investigate the application of machine learning techniques in requirement engineering activitiesCAAI Transactions on Intelligence Technology10.1049/cit2.123489:6(1412-1434)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1049/cit2.12348
Frattini JFucci DTorkar RMontgomery LUnterkalmsteiner MFischbach JMendez D(2024)Applying bayesian data analysis for causal inference about requirements quality: a controlled experimentEmpirical Software Engineering10.1007/s10664-024-10582-130:1Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1007/s10664-024-10582-1
Umar MLano K(2024)Advances in automated support for requirements engineering: a systematic literature reviewRequirements Engineering10.1007/s00766-023-00411-029:2(177-207)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00766-023-00411-0
Frattini JMontgomery LFischbach JMendez DFucci DUnterkalmsteiner M(2023)Requirements quality research: a harmonized theory, evaluation, and roadmapRequirements Engineering10.1007/s00766-023-00405-y28:4(507-520)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00766-023-00405-y
Adanza Dopazo DMoreno Pelayo VGénova Fuster G(2021)An automatic methodology for the quality enhancement of requirements using genetic algorithmsInformation and Software Technology10.1016/j.infsof.2021.106696140:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.infsof.2021.106696
Ahmad AFeng CKhan MKhan AUllah ANazir STahir A(2020)A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack OverflowSecurity and Communication Networks10.1155/2020/88306832020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8830683
Moreno VGénova GParra EFraga A(2020)Application of machine learning techniques to the flexible assessment and improvement of requirements qualitySoftware Quality Journal10.1007/s11219-020-09511-428:4(1645-1674)Online publication date: 27-Apr-2020
https://dl.acm.org/doi/10.1007/s11219-020-09511-4
Antinyan VStaron M(2017)RendexJournal of Systems and Software10.1016/j.jss.2017.05.079131:C(63-77)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.jss.2017.05.079

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