Jing Jiang

Dr. Jing Jiang


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 May 2016 in Empirical Software Engineering
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Journal Article
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JJJing Jiang
Jing Jiang
DLDavid Lo
David Lo
JHJiahuan He
Jiahuan He
XXXin Xia
Xin Xia
PKPavneet Singh Kochhar
Pavneet Singh Kochhar
LZLi Zhang
Li Zhang

12 September 2018 in Journal of Computer Science and Technology
Verified
Journal Article
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LZLi Zhang
Li Zhang
JTJia-Hao Tian
Jia-Hao Tian
JJJing Jiang
Jing Jiang
YLYi-Jun Liu
Yi-Jun Liu
MPMeng-Yuan Pu
Meng-Yuan Pu
TYTao Yue
Tao Yue

14 September 2015 in Journal of Computer Science and Technology
Verified
Journal Article
Closed access
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JJJing Jiang
Jing Jiang
JHJia-Huan He
Jia-Huan He
XCXue-Yuan Chen
Xue-Yuan Chen

28 June 2021 in Journal of Software: Evolution and Process
Verified
Journal Article
Closed access
PDF file
YXYong Xu
Yong Xu
LBLili Bo
Lili Bo
XSXiaobing Sun
Xiaobing Sun
BLBin Li
Bin Li
JJJing Jiang
Jing Jiang
WZWei Zhou
Wei Zhou

4 August 2021 in IEEE Transactions on Reliability
Verified
Journal Article
Closed access
JJJing Jiang
Jing Jiang
JLJiangfeng Lv
Jiangfeng Lv
JZJiateng Zheng
Jiateng Zheng
LZLi Zhang
Li Zhang
In pull-based development process, contributors submit their code to open-source projects by pull requests, which are accepted or rejected by reviewers. Contributors may modify their code, which causes several iterations of code review process, and makes code reviews time-consuming for both contributors and reviewers. In this article, we set out to study pull request modifications in a code review process. We collect nine projects on GitHub with 104 307 pull requests, and investigate pull request modifications through analyzing added commits after pull requests’ submission. By studying four research questions, we conclude our major findings as follow. First, 34.56 $\%$ of collected pull requests have modifications. Pull requests with modifications have longer lifetime but higher pass rates. Second, we conclude eight modification types indicating why pull requests are modified. Third, we propose a novel method called MClassify to automatically classify pull request modifications, which achieves the accuracy of 0.807. Fourth, various modification types affect code review differently from the perspective of lifetime and pass rate. Pull requests with source control system management modifications have the longest lifetime. These findings enable developers and researchers to understand a pull-based code review process better and make improvements.

20 April 2024 in ACM Transactions on Software Engineering and Methodology
Verified
Journal Article
Open access
XTXin Tan
Xin Tan
XLXinyue Lv
Xinyue Lv
JJJing Jiang
Jing Jiang
LZLi Zhang
Li Zhang
Real-time collaborative programming (RCP) entails developers working simultaneously, regardless of their geographic locations. RCP differs from traditional asynchronous online programming methods, such as Git or SVN, where developers work independently and update the codebase at separate times. Although various real-time code collaboration tools (e.g., Visual Studio Live Share , Code with Me , and Replit ) have kept emerging in recent years, none of the existing studies explicitly focus on a deep understanding of the processes or experiences associated with RCP. To this end, we combine interviews and an e-mail survey with the users of Visual Studio Live Share , aiming to understand (i) the scenarios, (ii) the requirements, and (iii) the challenges when developers participate in RCP. We find that developers participate in RCP in 18 different scenarios belonging to six categories, e.g., pair programming , group debugging , and code review . However, existing users’ attitudes toward the usefulness of the current RCP tools in these scenarios were significantly more negative than the expectations of potential users. As for the requirements, the most critical category is live editing , followed by the need for sharing terminals to enable hosts and guests to run commands and see the results, as well as focusing and following , which involves “following” the host’s edit location and “focusing” the guests’ attention on the host with a notification. Under these categories, we identify 17 requirements, but most of them are not well supported by current tools. In terms of challenges, we identify 19 challenges belonging to seven categories. The most severe category of challenges is lagging followed by permissions and conflicts . The above findings indicate that the current RCP tools and even collaborative environment need to be improved greatly and urgently. Based on these findings, we discuss the recommendations for different stakeholders, including practitioners, tool designers, and researchers.