Ge Li

5.1k total citations · 4 hit papers
108 papers, 2.2k citations indexed

About

Ge Li is a scholar working on Information Systems, Artificial Intelligence and Software. According to data from OpenAlex, Ge Li has authored 108 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Information Systems, 45 papers in Artificial Intelligence and 42 papers in Software. Recurrent topics in Ge Li's work include Software Engineering Research (60 papers), Software Testing and Debugging Techniques (29 papers) and Topic Modeling (18 papers). Ge Li is often cited by papers focused on Software Engineering Research (60 papers), Software Testing and Debugging Techniques (29 papers) and Topic Modeling (18 papers). Ge Li collaborates with scholars based in China, Australia and United States. Ge Li's co-authors include Zhi Jin, Xin Xia, Xing Hu, David Lo, Wenhan Wang, Zhi Ding, Bo Ma, Fang Liu, Jia Li and Yunfei Zhao and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Transactions on Software Engineering and Remote Sensing.

In The Last Decade

Ge Li

95 papers receiving 2.2k citations

Hit Papers

Deep code comment generation 2018 2026 2020 2023 2018 2019 2024 2024 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep code comment generation
    2018 391 citations Xing Hu, Ge Li et al. profile →
  2. Deep code comment generation with hybrid lexical and syntactical information
    2019 201 citations Xing Hu, Ge Li et al. profile →
  3. Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
    2024 78 citations Mingyang Geng, Shangwen Wang et al. profile →
  4. Self-Collaboration Code Generation via ChatGPT
    2024 69 citations Yihong Dong, Xue Jiang et al. ACM Transactions on Software Engineering and Methodology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ge Li China 25 1.6k 1.0k 819 532 406 108 2.2k
Daya Guo China 11 1.6k 1.0× 1.3k 1.3× 766 0.9× 483 0.9× 380 0.9× 21 2.4k
Abdelwahab Hamou‐Lhadj Canada 23 1.1k 0.7× 623 0.6× 659 0.8× 410 0.8× 926 2.3× 140 1.7k
Linjun Shou China 14 1.1k 0.7× 1.2k 1.2× 521 0.6× 351 0.7× 269 0.7× 39 2.0k
Işıl Dillig United States 27 1.3k 0.8× 1.2k 1.2× 1.2k 1.4× 717 1.3× 700 1.7× 98 2.4k
Benoît Baudry France 27 1.4k 0.9× 1.0k 1.0× 1.5k 1.9× 237 0.4× 544 1.3× 126 2.3k
Anna Rita Fasolino Italy 27 1.8k 1.1× 541 0.5× 1.5k 1.9× 836 1.6× 780 1.9× 105 2.6k
Spiros Mancoridis United States 23 2.1k 1.3× 1.3k 1.2× 976 1.2× 332 0.6× 985 2.4× 83 2.5k
Lingxiao Jiang Singapore 25 2.6k 1.6× 654 0.6× 1.7k 2.1× 1.0k 1.9× 577 1.4× 104 3.1k
Houari Sahraoui Canada 32 2.5k 1.5× 947 0.9× 1.6k 1.9× 312 0.6× 774 1.9× 143 2.9k

Countries citing papers authored by Ge Li

Since Specialization
Citations

This map shows the geographic impact of Ge Li'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 Ge Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ge Li more than expected).

Fields of papers citing papers by Ge Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ge Li. 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 Ge Li. The network helps show where Ge Li may publish in the future.

Co-authorship network of co-authors of Ge Li

This figure shows the co-authorship network connecting the top 25 collaborators of Ge Li. A scholar is included among the top collaborators of Ge Li 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 Ge Li. Ge Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dong, Yihong, et al.. (2025). Rethinking Repetition Problems of LLMs in Code Generation. 965–985.
2.
3.
Zhang, Yuwei, et al.. (2025). PATCH : Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing. ACM Transactions on Software Engineering and Methodology. 35(1). 1–35.
4.
5.
Tao, Chongyang, et al.. (2025). Large Language Model-Aware In-Context Learning for Code Generation. ACM Transactions on Software Engineering and Methodology. 2 indexed citations
6.
Jiang, Xue, et al.. (2025). Exploring Data-Efficient Adaptation of Large Language Models for Code Generation. ACM Transactions on Software Engineering and Methodology.
7.
Zhang, Huangzhao, Zhuo Li, Jia Li, Zhi Jin, & Ge Li. (2024). WELL: Applying bug detectors to bug localization via weakly supervised learning. Journal of Software Evolution and Process. 36(9).
8.
Jiang, Xue, et al.. (2024). Self-Planning Code Generation with Large Language Models. ACM Transactions on Software Engineering and Methodology. 33(7). 1–30. 33 indexed citations
9.
Dong, Yihong, Xue Jiang, Zhi Jin, & Ge Li. (2024). Self-Collaboration Code Generation via ChatGPT. ACM Transactions on Software Engineering and Methodology. 33(7). 1–38. 69 indexed citations breakdown →
10.
Geng, Mingyang, Shangwen Wang, Dezun Dong, et al.. (2024). Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning. 1–13. 78 indexed citations breakdown →
11.
Zhang, Hongyu, et al.. (2024). IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion. Proceedings of the ACM on software engineering.. 1(FSE). 182–203. 4 indexed citations
12.
Fu, Zhiyi, et al.. (2024). Non-Autoregressive Line-Level Code Completion. ACM Transactions on Software Engineering and Methodology. 33(5). 1–34. 13 indexed citations
13.
Jin, Zhi, et al.. (2024). Knowledge-Aware Code Generation with Large Language Models. 52–63. 6 indexed citations
14.
Dong, Yihong, et al.. (2024). CodeScore: Evaluating Code Generation by Learning Code Execution. ACM Transactions on Software Engineering and Methodology. 34(3). 1–22. 21 indexed citations
15.
Li, Jia, et al.. (2024). Structured Chain-of-Thought Prompting for Code Generation. ACM Transactions on Software Engineering and Methodology. 34(2). 1–23. 37 indexed citations
16.
Jin, Zhi, et al.. (2024). KareCoder: A New Knowledge-Enriched Code Generation System. 270–271. 2 indexed citations
17.
Yang, Zhen, Jacky Keung, Zeyu Sun, et al.. (2023). Improving domain-specific neural code generation with few-shot meta-learning. Information and Software Technology. 166. 107365–107365. 4 indexed citations
18.
Li, Jia, Ge Li, Zhuo Li, et al.. (2023). CodeEditor : Learning to Edit Source Code with Pre-trained Models. ACM Transactions on Software Engineering and Methodology. 32(6). 1–22. 20 indexed citations
19.
Li, Jia, Zhuo Li, Huangzhao Zhang, et al.. (2023). Poison Attack and Poison Detection on Deep Source Code Processing Models. ACM Transactions on Software Engineering and Methodology. 33(3). 1–31. 11 indexed citations
20.
Zhang, Huangzhao, Zhiyi Fu, Ge Li, et al.. (2022). Towards Robustness of Deep Program Processing Models—Detection, Estimation, and Enhancement. ACM Transactions on Software Engineering and Methodology. 31(3). 1–40. 33 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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