Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-18599
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDi Sorbo, Andrea-
dc.contributor.authorPanichella, Sebastiano-
dc.contributor.authorVisaggio, Corrado Aaron-
dc.contributor.authorDi Penta, Massimiliano-
dc.contributor.authorCanfora, Gerardo-
dc.contributor.authorGall, Harald C.-
dc.date.accessioned2019-10-31T14:04:50Z-
dc.date.available2019-10-31T14:04:50Z-
dc.date.issued2019-
dc.identifier.issn0098-5589de_CH
dc.identifier.issn1939-3520de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/18599-
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractCommunication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed DECA (Development Emails Content Analyzer), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with a study involving Master's and Ph.D. students, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named NEON (Nlp-based softwarE dOcumentation aNalyzer), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns' identification and rules' definition processes, allowing a savings of more than 70% of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Software Engineeringde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleExploiting natural language structures in software informal documentationde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
dc.identifier.doi10.1109/TSE.2019.2930519de_CH
dc.identifier.doi10.21256/zhaw-18599-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedService Engineeringde_CH
zhaw.author.additionalNode_CH
Appears in Collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
08769918.pdf8.13 MBAdobe PDFThumbnail
View/Open


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