User Stories identification in software's issues records using natural language processing

Autores
Peña Veitía, Francisco J.; Roldán, María Luciana; Vegetti, María Marcela
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Nowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.
Fil: Pendiente completar
Fil: Peña Veitía, Francisco J. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
Fil: Roldán, María Luciana. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
Fil: Vegetti, María Marcela. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
Materia
Natural Language Processing
Machine Learning
Recurrent Neural Networks
Software Engineering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-03-26T20:57:02Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/10135
Acceder
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oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/10135
network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling User Stories identification in software's issues records using natural language processingPeña Veitía, Francisco J.Roldán, María LucianaVegetti, María MarcelaNatural Language ProcessingMachine LearningRecurrent Neural NetworksSoftware EngineeringNowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.Fil: Pendiente completarFil: Peña Veitía, Francisco J. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.Fil: Roldán, María Luciana. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.Fil: Vegetti, María Marcela. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.V ARGENCON2024-03-26T20:57:02Z2024-03-26T20:57:02Z2020-12info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciapdfapplication/pdfPeña Veitía, F.J.; Roldán, L. & Vegetti, M. (2020). User Stories identification in software's issues records using natural language processing. 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentinahttp://hdl.handle.net/20.500.12272/1013510.1109/ARGENCON49523.2020.9505355engSIUTIFE0005514TC - Sistema de recomendación de prácticas ágiles basado en ontologías y aprendizaje automáticoinfo:eu-repo/semantics/openAccess2024-03-26T20:57:02Zhttp://creativecommons.org/licenses/by/4.0/Atribución 4.0 InternacionalLos autoresCreativeCommonsreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:46:39Zoai:ria.utn.edu.ar:20.500.12272/10135instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:46:40.153Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv User Stories identification in software's issues records using natural language processing
title User Stories identification in software's issues records using natural language processing
spellingShingle User Stories identification in software's issues records using natural language processing
Peña Veitía, Francisco J.
Natural Language Processing
Machine Learning
Recurrent Neural Networks
Software Engineering
title_short User Stories identification in software's issues records using natural language processing
title_full User Stories identification in software's issues records using natural language processing
title_fullStr User Stories identification in software's issues records using natural language processing
title_full_unstemmed User Stories identification in software's issues records using natural language processing
title_sort User Stories identification in software's issues records using natural language processing
dc.creator.none.fl_str_mv Peña Veitía, Francisco J.
Roldán, María Luciana
Vegetti, María Marcela
author Peña Veitía, Francisco J.
author_facet Peña Veitía, Francisco J.
Roldán, María Luciana
Vegetti, María Marcela
author_role author
author2 Roldán, María Luciana
Vegetti, María Marcela
author2_role author
author
dc.subject.none.fl_str_mv Natural Language Processing
Machine Learning
Recurrent Neural Networks
Software Engineering
topic Natural Language Processing
Machine Learning
Recurrent Neural Networks
Software Engineering
dc.description.none.fl_txt_mv Nowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.
Fil: Pendiente completar
Fil: Peña Veitía, Francisco J. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
Fil: Roldán, María Luciana. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
Fil: Vegetti, María Marcela. CONICET-UTN. Instituto de desarrollo y diseño (INGAR); Argentina.
description Nowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.
publishDate 2020
dc.date.none.fl_str_mv 2020-12
2024-03-26T20:57:02Z
2024-03-26T20:57:02Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv Peña Veitía, F.J.; Roldán, L. & Vegetti, M. (2020). User Stories identification in software's issues records using natural language processing. 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentina
http://hdl.handle.net/20.500.12272/10135
10.1109/ARGENCON49523.2020.9505355
identifier_str_mv Peña Veitía, F.J.; Roldán, L. & Vegetti, M. (2020). User Stories identification in software's issues records using natural language processing. 2020 IEEE Congreso Bienal de Argentina (ARGENCON), Resistencia, Argentina
10.1109/ARGENCON49523.2020.9505355
url http://hdl.handle.net/20.500.12272/10135
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv SIUTIFE0005514TC - Sistema de recomendación de prácticas ágiles basado en ontologías y aprendizaje automático
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-03-26T20:57:02Z
http://creativecommons.org/licenses/by/4.0/
Atribución 4.0 Internacional
Los autores
CreativeCommons
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-03-26T20:57:02Z
http://creativecommons.org/licenses/by/4.0/
Atribución 4.0 Internacional
Los autores
CreativeCommons
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv V ARGENCON
publisher.none.fl_str_mv V ARGENCON
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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