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Publikationstyp: Beitrag in wissenschaftlicher Zeitschrift
Art der Begutachtung: Peer review (Publikation)
Titel: User review-based change file localization for mobile applications
Autor/-in: Zhou, Yu
Su, Yanqi
Chen, Taolue
Huang, Zhiqiu
Gall, Harald C.
Panichella, Sebastiano
et. al: No
DOI: 10.1109/TSE.2020.2967383
10.21256/zhaw-19314
Erschienen in: IEEE Transactions on Software Engineering
Erscheinungsdatum: 2020
Verlag / Hrsg. Institution: IEEE
ISSN: 0098-5589
1939-3520
2326-3881
Sprache: Englisch
Schlagwörter: User review; Mobile app; Information retrieval; Change file localization
Fachgebiet (DDC): 005: Computerprogrammierung, Programme und Daten
Zusammenfassung: In 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.
Weitere Angaben: © 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/19314
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Institut für Angewandte Informationstechnologie (InIT)
Enthalten in den Sammlungen:Publikationen School of Engineering

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