TY - JOUR
T1 - Reviewing rounds prediction for code patches
AU - Huang, Yuan
AU - Liang, Xingjian
AU - Chen, Zhihao
AU - Jia, Nan
AU - Luo, Xiapu
AU - Chen, Xiangping
AU - Zheng, Zibin
AU - Zhou, Xiaocong
N1 - Funding Information:
The work is supported by the Key-Area Research and Development Program of Guangdong Province (No. 2020B010164002), the National Natural Science Foundation of China (Nos. 61902441, 61902105, 62032025), HK RGC Project (No. PolyU 152239/18E), Guangdong Basic and Applied Basic Research Foundation(No. 2020A-1515010973), the Fundamental Research Funds for the Central Universities (No. 20lgpy129). Xiangping Chen is the corresponding author.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Code patch
KW - Code review
KW - Discriminative feature
KW - Machine learning
KW - Reviewing rounds
UR - http://www.scopus.com/inward/record.url?scp=85117702514&partnerID=8YFLogxK
U2 - 10.1007/s10664-021-10035-z
DO - 10.1007/s10664-021-10035-z
M3 - Journal article
AN - SCOPUS:85117702514
SN - 1382-3256
VL - 27
SP - 1
EP - 40
JO - Empirical Software Engineering
JF - Empirical Software Engineering
IS - 1
M1 - 7
ER -