Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19314
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dc.contributor.authorZhou, Yu-
dc.contributor.authorSu, Yanqi-
dc.contributor.authorChen, Taolue-
dc.contributor.authorHuang, Zhiqiu-
dc.contributor.authorGall, Harald C.-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2020-01-30T13:09:21Z-
dc.date.available2020-01-30T13:09:21Z-
dc.date.issued2020-
dc.identifier.issn0098-5589de_CH
dc.identifier.issn1939-3520de_CH
dc.identifier.issn2326-3881de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19314-
dc.description© 2020 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.abstractIn the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Reviews Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.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.subjectUser reviewde_CH
dc.subjectMobile appde_CH
dc.subjectInformation retrievalde_CH
dc.subjectChange file localizationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleUser review-based change file localization for mobile applicationsde_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.2020.2967383de_CH
dc.identifier.doi10.21256/zhaw-19314-
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

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