Improved prioritization of software development demands in Turkish with deep learning-based NLP

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dc.authorid0000-0002-2735-7996en_US
dc.contributor.authorTunalı, Volkan
dc.date.accessioned2024-07-12T20:59:22Z
dc.date.available2024-07-12T20:59:22Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractManagement of software development demands including bug or defect fixes and new feature or change requests is a crucial part of software maintenance. Failure to prioritize demands correctly might result in inefficient planning and use of resources as well as user or customer dissatisfaction. In order to overcome the difficulty and inefficiency of manual processing, many automated prioritization approaches were proposed in the literature. However, existing body of research generally focused on bug report repositories of open-source software, where textual bug descriptions are in English. Additionally, they proposed solutions to the problem using mostly classical text mining methods and machine learning (ML) algorithms. In this study, we first introduce a demand prioritization dataset in Turkish, which is composed of manually labeled demand records taken from the demand management system of a private insurance company in Turkey. Second, we propose several deep learning (DL) architectures to improve software development demand prioritization. Through an extensive experimentation, we compared the effectiveness of our DL architectures trained with several combinations of different optimizers and activation functions in order to reveal the best combination for demand prioritization in Turkish. We empirically show that DL models can achieve much higher accuracy than classical ML models even with a small amount of training data.en_US
dc.identifier.citationTunalı, V. (2022). Improved prioritization of software development demands in Turkish with deep learning-based NLP. IEEE Access, 10, p.40249 - 40263.en_US
dc.identifier.doi10.1109/access.2022.3167269
dc.identifier.endpage40263en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85128309218en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage40249en_US
dc.identifier.urihttps://doi.prg/10.1109/access.2022.3167269
dc.identifier.urihttps://hdl.handle.net/20.500.12415/3286
dc.identifier.volume10en_US
dc.identifier.wosWOS:000785727400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIEEE Accessen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryUluslararası Hakemli Dergide Makale - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY01209
dc.subjectSoftware engineeringen_US
dc.subjectDemand prioritizationen_US
dc.subjectBug prioritizationen_US
dc.subjectMachine learningen_US
dc.subjectText classificationen_US
dc.subjectDeep learningen_US
dc.titleImproved prioritization of software development demands in Turkish with deep learning-based NLPen_US
dc.typeArticle
dspace.entity.typePublication

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