Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/337728
Full metadata record
DC FieldValueLanguage
dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampTorres, Ricardo da Silva-
dc.contributor.authorunicampCortês, Mario Lúcio-
dc.typeArtigopt_BR
dc.titleBug report severity level prediction in open source software : a survey and research opportunitiespt_BR
dc.contributor.authorGomes, Luiz Alberto Ferreira-
dc.contributor.authorTorres, Ricardo da Silva-
dc.contributor.authorCôrtes, Mario Lúcio-
dc.subjectSoftware - Manutençãopt_BR
dc.subjectAprendizado de máquinapt_BR
dc.subject.otherlanguageSoftware - Maintenancept_BR
dc.subject.otherlanguageMachine learningpt_BR
dc.description.abstractThe severity level attribute of a bug report is considered one of the most critical variables for planning evolution and maintenance in Free/Libre Open Source Software. This variable measures the impact the bug has on the successful execution of the software system and how soon a bug needs to be addressed by the development team. Both business and academic community have made an extensive investigation towards the proposal methods to automate the bug report severity prediction. This paper aims to provide a comprehensive mapping study review of recent research efforts on automatically bug report severity prediction. To the best of our knowledge, this is the first review to categorize quantitatively more than ten aspects of the experiments reported in several papers on bug report severity prediction. The mapping study review was performed by searching four electronic databases. Studies published until December 2017 were considered. The initial resulting comprised of 54 papers. From this set, a total of 18 papers were selected. After performing snowballing, more nine papers were selected. From the mapping study, we identified 27 studies addressing bug report severity prediction on Free/Libre Open Source Software. The gathered data confirm the relevance of this topic, reflects the scientific maturity of the research area, as well as, identify gaps, which can motivate new research initiatives. The message drawn from this review is that unstructured text features along with traditional machine learning algorithms and text mining methods have been playing a central role in the most proposed methods in literature to predict bug severity level. This scenario suggests that there is room for improving prediction results using state-of-the-art machine learning and text mining algorithms and techniquespt_BR
dc.relation.ispartofInformation and software technologypt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2019-11-
dc.date.monthofcirculationNov.pt_BR
dc.language.isoengpt_BR
dc.description.volume115pt_BR
dc.description.firstpage58pt_BR
dc.description.lastpage78pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0950-5849pt_BR
dc.identifier.eissn1873-6025pt_BR
dc.identifier.doi10.1016/j.infsof.2019.07.009pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0950584919301648pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber307560/2016-3pt_BR
dc.description.sponsordocumentnumber88881.145912/2017-0; 001pt_BR
dc.description.sponsordocumentnumber2014/12236-1; 2015/24494-8; 2016/50250-1; 2017/20945-0pt_BR
dc.date.available2020-03-30T13:43:43Z-
dc.date.accessioned2020-03-30T13:43:43Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-03-30T13:43:43Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-07-20T14:19:07Z : No. of bitstreams: 1 000485851700006.pdf: 1579646 bytes, checksum: 2d8faff0c889eb24289085e15d363df9 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-03-30T13:43:43Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/337728-
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordBug tracking systemspt_BR
dc.subject.keywordBug reportspt_BR
dc.subject.keywordSeverity level predictionpt_BR
dc.subject.keywordSystematic mappingpt_BR
dc.identifier.source000485851700006pt_BR
dc.creator.orcid0000-0001-9772-263Xpt_BR
dc.creator.orcid0000-0002-3891-1593pt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteAuthors are grateful to CAPES (grant #88881.145912/2017-01), CNPq (grant #307560/2016-3), FAPESP (grants #2014/12236-1, #2015/24494-8, #2016/50250-1, and #2017/20945-0), the FAPESP-Microsoft Virtual Institute (grants #2013/50155-0, #2013/50169-1, and #2014/50715-9) and The Pontifical Catholic University of Minas Gerais by support to the first author through the Permanent Program For Professor Qualification (PPCD). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001pt_BR
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
000485851700006.pdf1.54 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.