Kui Liu

2.3k total citations
71 papers, 1.2k citations indexed

About

Kui Liu is a scholar working on Information Systems, Software and Signal Processing. According to data from OpenAlex, Kui Liu has authored 71 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Information Systems, 33 papers in Software and 14 papers in Signal Processing. Recurrent topics in Kui Liu's work include Software Engineering Research (35 papers), Software Testing and Debugging Techniques (27 papers) and Software Reliability and Analysis Research (24 papers). Kui Liu is often cited by papers focused on Software Engineering Research (35 papers), Software Testing and Debugging Techniques (27 papers) and Software Reliability and Analysis Research (24 papers). Kui Liu collaborates with scholars based in China, Luxembourg and Australia. Kui Liu's co-authors include Tegawendé F. Bissyandé, Dongsun Kim, Anil Koyuncu, Li Li, Nasser Kehtarnavaz, Jacques Klein, Roozbeh Jafari, Yves Le Traon, Chen Chen and Pingfan Kong and has published in prestigious journals such as Sensors, ACM Computing Surveys and IEEE Transactions on Software Engineering.

In The Last Decade

Kui Liu

63 papers receiving 1.2k citations

Peers

Kui Liu

SOFTW

CNC

Jonathan de Halleux United States

Manjari Gupta India

Yogachandran Rahulamathavan United Kingdom

Ling Xu China

Laurence Tratt United Kingdom

Xiang Long China

Peijun Ma China

İsmail Arı Türkiye

Jonathan de Halleux United States

Kui Liu

813 ×1.1

917 ×1.5

SOFTW

272 ×1.0

191 ×0.9

CNC

117 ×0.6

Citations per year, relative to Kui Liu Kui Liu (= 1×) peers Jonathan de Halleux

Countries citing papers authored by Kui Liu

Since Specialization

Citations

This map shows the geographic impact of Kui Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kui Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kui Liu more than expected).

Fields of papers citing papers by Kui Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kui Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kui Liu. The network helps show where Kui Liu may publish in the future.

Co-authorship network of co-authors of Kui Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Kui Liu. A scholar is included among the top collaborators of Kui Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kui Liu. Kui Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Hu, Xing, et al.. (2025). Towards Explainable Vulnerability Detection With Large Language Models. IEEE Transactions on Software Engineering. 51(10). 2957–2971.

Hu, Xing, et al.. (2025). Understanding Practitioners’ Expectations on Clear Code Review Comments. Proceedings of the ACM on software engineering.. 2(ISSTA). 1257–1279. 2 indexed citations

Wang, Shaowei, et al.. (2025). Code Change Intention, Development Artifact, and History Vulnerability: Putting Them Together for Vulnerability Fix Detection by LLM. Proceedings of the ACM on software engineering.. 2(FSE). 489–510. 2 indexed citations

Ni, Chao, et al.. (2025). Distinguishing LLM-Generated from Human-Written Code by Contrastive Learning. ACM Transactions on Software Engineering and Methodology. 34(4). 1–31. 1 indexed citations

Gao, Cuiyun, et al.. (2025). An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities. ACM Transactions on Software Engineering and Methodology. 4 indexed citations

Li, Zhong, et al.. (2025). PReMM: LLM-Based Program Repair for Multi-method Bugs via Divide and Conquer. Proceedings of the ACM on Programming Languages. 9(OOPSLA2). 1316–1344.

Liu, Kui, et al.. (2024). Green gentrification and who will benefit from green infrastructure regeneration? A quasi-experimental study in China. Cities. 153. 105307–105307. 7 indexed citations

Wu, Di, et al.. (2024). iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring. 1345–1357. 3 indexed citations

Zhang, Lianming, et al.. (2023). A data-driven network intrusion detection system using feature selection and deep learning. Journal of Information Security and Applications. 78. 103606–103606. 17 indexed citations

10.

Yahya, Ali Abdullah, et al.. (2023). A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function. Sensors. 23(6). 2976–2976. 11 indexed citations

11.

Liu, Kui, Li Li, Anil Koyuncu, et al.. (2023). Reliable Fix Patterns Inferred from Static Checkers for Automated Program Repair. ACM Transactions on Software Engineering and Methodology. 32(4). 1–38. 5 indexed citations

12.

Liu, Xiao, Yao Wan, Yanjie Zhao, et al.. (2023). PyScribe–Learning to describe python code. Software Practice and Experience. 54(3). 501–527. 1 indexed citations

13.

Kim, Kisub, Dongsun Kim, Xin Zhou, et al.. (2023). Big Code Search: A Bibliography. ACM Computing Surveys. 56(1). 1–49. 9 indexed citations

14.

Liu, Kui, et al.. (2022). Crex: Predicting patch correctness in automated repair of C programs through transfer learning of execution semantics. Information and Software Technology. 152. 107043–107043. 4 indexed citations

15.

Li, Chuanyi, et al.. (2022). StandUp4NPR: Standardizing SetUp for Empirically Comparing Neural Program Repair Systems. 1–13. 14 indexed citations

16.

Liu, Kui, et al.. (2020). On the Need of Understanding the Failures of Smart Contracts. IEEE Software. 37(5). 49–54. 3 indexed citations

17.

Li, Li, et al.. (2020). Exploring how deprecated Python library APIs are (not) handled. 233–244. 33 indexed citations

18.

Liu, Kui, Anil Koyuncu, Dongsun Kim, & Tegawendé F. Bissyandé. (2019). AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations. Open Repository and Bibliography (University of Luxembourg). 1–12. 112 indexed citations

19.

Kong, Pingfan, Li Li, Jun Gao, et al.. (2018). Automated Testing of Android Apps: A Systematic Literature Review. IEEE Transactions on Reliability. 68(1). 45–66. 138 indexed citations

20.

Liu, Kui, Dongsun Kim, Tegawendé F. Bissyandé, Shin Yoo, & Yves Le Traon. (2018). Mining Fix Patterns for FindBugs Violations. IEEE Transactions on Software Engineering. 47(1). 165–188. 79 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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