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Application of machine learning techniques to the flexible assessment and improvement of requirements quality

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Knowledge Reusinges
dc.contributor.authorMoreno Pelayo, Valentín
dc.contributor.authorGénova Fuster, Gonzalo
dc.contributor.authorParra Corredor, Eugenio
dc.contributor.authorFraga Vázquez, Anabel
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-02-18T15:38:40Z
dc.date.available2022-02-18T15:38:40Z
dc.date.issued2020-04-27
dc.description.abstractIt is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality. In this work, we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations, and quality standards, with a high degree of automation. The domain experts contribute with an initial set of requirements that they have classified according to their quality, and we extract their quality metrics. We then use machine learning techniques to emulate the implicit expert’s quality function. We provide also a procedure to suggest improvements in bad requirements. We compare the obtained rule-based classifiers with different machine learning algorithms, obtaining measurements of effectiveness around 85%. We show as well the appearance of the generated rules and how to interpret them. The method is tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automateden
dc.description.sponsorshipThis research has received funding from the CRYSTAL project–Critical System Engineering Acceleration (European Union’s Seventh Framework Program FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement no 332830); and from the AMASS project–Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262).en
dc.identifier.bibliographicCitationMoreno, V., Génova, G., Parra, E. et al. Application of machine learning techniques to the flexible assessment and improvement of requirements quality. Software Qual J 28, 1645–1674 (2020). https://doi.org/10.1007/s11219-020-09511-4en
dc.identifier.doihttps://doi.org/10.1007/s11219-020-09511-4
dc.identifier.issn0963-9314
dc.identifier.publicationfirstpage1645
dc.identifier.publicationissue4
dc.identifier.publicationlastpage1674
dc.identifier.publicationtitleSOFTWARE QUALITY JOURNALen
dc.identifier.publicationvolume28
dc.identifier.urihttps://hdl.handle.net/10016/34169
dc.identifier.uxxiAR/0000027743
dc.language.isoengen
dc.publisherSpringeren
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/ART-010000-2013-1en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/692474en
dc.relation.projectIDGobierno de España. PCIN-2015-262es
dc.rights© 2020, Springer Science Business Media, LLC, part of Springer Natureen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherrequirements qualityen
dc.subject.othermachine learningen
dc.subject.otherautomatic classificationen
dc.subject.otherautomatic improvementen
dc.subject.otherexperts judgmenten
dc.subject.otherflexible assessmenten
dc.titleApplication of machine learning techniques to the flexible assessment and improvement of requirements qualityen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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