Anaphoric Ambiguity Resolution in Software Requirement Texts

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dc.authorscopusid57928821600
dc.authorscopusid23096618000
dc.authorscopusid56251503100
dc.authorscopusid57207576254
dc.contributor.authorJafari, S.M.
dc.contributor.authorYildirim, S.
dc.contributor.authorCevik, M.
dc.contributor.authorBasar, A.
dc.date.accessioned2024-07-18T20:17:03Z
dc.date.available2024-07-18T20:17:03Z
dc.date.issued2023
dc.descriptionAnkura;IEEE Dataporten_US
dc.description2023 IEEE International Conference on Big Data, BigData 2023 -- 15 December 2023 through 18 December 2023 -- -- 196820en_US
dc.description.abstractIn requirements engineering (RE), anaphoric ambiguity is a frequent cause of misunderstandings. It can have a detrimental effect on the quality of requirements and jeopardize the success of a project. If stakeholders of the system, such as testers, developers, or customers, have different understandings or interpretations of software requirements, the system may not be accepted during customer validation. Despite its significance, there has been limited investigation into anaphoric ambiguity in RE. However, focusing on both recognizing and solving uncertainty can be more advantageous than just identifying it. Therefore we investigated the effectiveness of various QA learning techniques including encoder-based and text generation-based NLP models for two goals. We conduct detailed numerical experiments using various transformer models on two public requirements datasets and one generic dataset. Our results indicated that our QA architecture exhibits superior performance compared to baseline models in detecting ambiguity as well as resolving anaphora in contrast to other baseline approaches. We showed that our developed architecture can automatically support requirement development to minimize interpretation risk between stakeholders. © 2023 IEEE.en_US
dc.identifier.doi10.1109/BigData59044.2023.10386192
dc.identifier.endpage4730en_US
dc.identifier.isbn9798350324457
dc.identifier.scopus2-s2.0-85184983547en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage4722en_US
dc.identifier.urihttps://doi.org/10.1109/BigData59044.2023.10386192
dc.identifier.urihttps://hdl.handle.net/11411/6392
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2023 IEEE International Conference on Big Data, BigData 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAmbiguityen_US
dc.subjectAnaphora Resolutionen_US
dc.subjectRequirements Engineeringen_US
dc.subjectTransformersen_US
dc.subjectLearning Systemsen_US
dc.subjectAmbiguityen_US
dc.subjectAmbiguity Resolutionen_US
dc.subjectAnaphora Resolutionen_US
dc.subjectLearning Techniquesen_US
dc.subjectNumerical Experimentsen_US
dc.subjectRequirement Engineeringen_US
dc.subjectSoftware Requirementsen_US
dc.subjectText Generationsen_US
dc.subjectTransformeren_US
dc.subjectUncertaintyen_US
dc.subjectRequirements Engineeringen_US
dc.titleAnaphoric Ambiguity Resolution in Software Requirement Texts
dc.typeConference Object

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