Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-8722
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dc.contributor.authorWinkler, Jonas Paul-
dc.contributor.authorGrönberg, Jannis-
dc.contributor.authorVogelsang, Andreas-
dc.date.accessioned2019-07-31T15:15:31Z-
dc.date.available2019-07-31T15:15:31Z-
dc.date.issued2019-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/9679-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-8722-
dc.description.abstract[Context] An important task in requirements engineering is to identify and determine how to verify a requirement (e.g., by manual review, testing, or simulation; also called potential verification method). This information is required to effectively create test cases and verification plans for requirements. [Objective] In this paper, we propose an automatic approach to classify natural language requirements with respect to their potential verification methods (PVM). [Method] Our approach uses a convolutional neural network architecture to implement a multiclass and multilabel classifier that assigns probabilities to a predefined set of six possible verification methods, which we derived from an industrial guideline. Additionally, we implemented a backtracing approach to analyze and visualize the reasons for the network’s decisions. [Results] In a 10-fold cross validation on a set of about 27,000 industrial requirements, our approach achieved a macro averaged F1 score of 0.79 across all labels. For the classification into test or non-test, the approach achieves an even higher F1 score of 0.94. [Conclusions] The results show that our approach might help to increase the quality of requirements specifications with respect to the PVM attribute and guide engineers in effectively deriving test cases and verification plans.en
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc004 Datenverarbeitung; Informatikde
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherrequirements engineeringen
dc.subject.otherrequirements validationen
dc.subject.othertest engineeringen
dc.subject.othermachine learningen
dc.subject.othernatural language processingen
dc.subject.otherneural networksen
dc.titlePredicting How to Test Requirements: An Automated Approachen
dc.typeConference Objecten
tub.accessrights.dnbdomainen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.proceedingstitle27th IEEE International Requirements Engineering Conference (RE'19)en
dcterms.bibliographicCitation.originalpublisherplaceJeju Islanden
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
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