Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/95642
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dc.contributorDepartment of Computingen_US
dc.creatorHuang, Yen_US
dc.creatorLiang, Xen_US
dc.creatorChen, Zen_US
dc.creatorJia, Nen_US
dc.creatorLuo, Xen_US
dc.creatorChen, Xen_US
dc.creatorZheng, Zen_US
dc.creatorZhou, Xen_US
dc.date.accessioned2022-09-27T02:46:30Z-
dc.date.available2022-09-27T02:46:30Z-
dc.identifier.urihttp://hdl.handle.net/10397/95642-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© The Author(s) 2021en_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Huang, Y., Liang, X., Chen, Z. et al. Reviewing rounds prediction for code patches. Empir Software Eng 27, 7 (2022) is available at https://doi.org/10.1007/s10664-021-10035-z.en_US
dc.subjectCode patchen_US
dc.subjectCode reviewen_US
dc.subjectDiscriminative featureen_US
dc.subjectMachine learningen_US
dc.subjectReviewing roundsen_US
dc.titleReviewing rounds prediction for code patchesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume27en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1007/s10664-021-10035-zen_US
dcterms.abstractCode review is one of the common activities to guarantee the reliability of software, while code review is time-consuming as it requires reviewers to inspect the source code of each patch. A patch may be reviewed more than once before it is eventually merged or abandoned, and then such a patch may tighten the development schedule of the developers and further affect the development progress of a project. Thus, a tool that predicts early on how long a patch will be reviewed can help developers take self-inspection beforehand for the patches that require long-time review. In this paper, we propose a novel method, PMCost, to predict the reviewing rounds of a patch. PMCost uses a number of features, including patch meta-features, code diff features, personal experience features and patch textual features, to better reflect code changes and review process. To examine the benefits of PMCost, we perform experiments on three large open source projects, namely Eclipse, OpenDaylight and OpenStack. The encouraging experimental results demonstrate the feasibility and effectiveness of our approach. Besides, we further study the why the proposed features contribute to the reviewing rounds prediction.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEmpirical software engineering, Jan. 2022, v. 27, no. 1, 7en_US
dcterms.isPartOfEmpirical software engineeringen_US
dcterms.issued2022-01-
dc.identifier.scopus2-s2.0-85117702514-
dc.identifier.ros2021002859-
dc.identifier.eissn1382-3256en_US
dc.identifier.artn7en_US
dc.description.validate202209 bchyen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberCDCF_2021-2022-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextKey-Area Research and Development Program of Guangdong Province; National Natural Science Foundation of China; Guangdong Basic and Applied Basic Research Foundation; Fundamental Research Funds for the Central Universitiesen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS67223459-
dc.description.oaCategoryCCen_US
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