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Title: Reviewing rounds prediction for code patches
Authors: Huang, Y
Liang, X
Chen, Z
Jia, N
Luo, X 
Chen, X
Zheng, Z
Zhou, X
Issue Date: Jan-2022
Source: Empirical software engineering, Jan. 2022, v. 27, no. 1, 7
Abstract: Code 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.
Keywords: Code patch
Code review
Discriminative feature
Machine learning
Reviewing rounds
Publisher: Springer
Journal: Empirical software engineering 
EISSN: 1382-3256
DOI: 10.1007/s10664-021-10035-z
Rights: © The Author(s) 2021
This 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/.
The 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.
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