Mingyang Geng
Impact in
- Software top 10%
- Software Testing and Debugging Techniques
- Information Systems top 10%
- Software Engineering Research
Papers in
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- Topic Modeling 5
- Domain Adaptation and Few-Shot Learning 3
- Natural Language Processing Techniques 2
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- Software Engineering Research 8
- Co-authors
- Shangwen Wang (5 shared papers)Xiaoguang Mao (4 shared papers)Zhi Jin (2 shared papers)Ge Li (1 shared paper)Dezun Dong (1 shared paper)Bo Ding (4 shared papers)Xiangke Liao (3 shared papers)Long Lan (4 shared papers)
In The Last Decade
Mingyang Geng
22 papers receiving 218 citations
Mingyang Geng's Hit Papers
Peers
Comparison fields: 5 of 49
- Software 50
- Information Systems 101
- Artificial Intelligence 102
- Computer Networks and Communications 54
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Mingyang Geng
This map shows the geographic impact of Mingyang Geng'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 Mingyang Geng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingyang Geng more than expected).
Fields of papers citing papers by Mingyang Geng
This network shows the impact of papers produced by Mingyang Geng. 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 Mingyang Geng. The network helps show where Mingyang Geng may publish in the future.
Co-authors
The 25 scholars most cited alongside Mingyang Geng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning Hit paper breakdown → | 2024 | 78 |
| 2 | 2022 | 26 | |
| 3 | 2019 | 17 | |
| 4 | 2019 | 17 | |
| 5 | 2023 | 11 | |
| 6 | 2021 | 10 | |
| 7 | 2022 | 9 | |
| 8 | 2023 | 9 | |
| 9 | 2023 | 7 | |
| 10 | 2024 | 6 | |
| 11 | 2020 | 6 | |
| 12 | 2018 | 5 | |
| 13 | 2019 | 5 | |
| 14 | 2019 | 5 | |
| 15 | 2024 | 4 | |
| 16 | 2021 | 2 | |
| 17 | 2025 | 1 | |
| 18 | 2023 | 1 | |
| 19 | 2024 | 1 | |
| 20 | 2025 | 1 |
About Mingyang Geng
Mingyang Geng is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Aerospace Engineering and Software, having authored 25 papers that have together received 223 indexed citations. Recurring topics across this work include Software Engineering Research (8 papers), Topic Modeling (5 papers), Software Reliability and Analysis Research (3 papers), Robotics and Sensor-Based Localization (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Advanced Malware Detection Techniques (3 papers), Natural Language Processing Techniques (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Software (50 citations), Information Systems (101 citations), Artificial Intelligence (102 citations), Computer Networks and Communications (54 citations) and Computer Vision and Pattern Recognition (42 citations). Mingyang Geng has collaborated with scholars based in China, Australia and Singapore. Frequent co-authors include Shangwen Wang, Xiaoguang Mao, Zhi Jin, Ge Li, Dezun Dong, Bo Ding, Xiangke Liao, Long Lan, Huaimin Wang and Huaimin Wang. Their work appears in journals such as Computers and Electronics in Agriculture, ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Software Engineering, Animals and Journal of Apicultural Research.
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.