He Ye

474 total citations · 1 hit paper
12 papers, 245 citations indexed

About

He Ye is a scholar working on Artificial Intelligence, Software and Information Systems. According to data from OpenAlex, He Ye has authored 12 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 6 papers in Software and 5 papers in Information Systems. Recurrent topics in He Ye's work include Software Testing and Debugging Techniques (6 papers), Software Engineering Research (4 papers) and Software Reliability and Analysis Research (3 papers). He Ye is often cited by papers focused on Software Testing and Debugging Techniques (6 papers), Software Engineering Research (4 papers) and Software Reliability and Analysis Research (3 papers). He Ye collaborates with scholars based in Sweden, United States and China. He Ye's co-authors include Martin Monperrus, Matías Martínez, Wanjun Zhong, Yanlin Wang, Qiqi Gao, Thomas Durieux, Jian Gu, Xiapu Luo, Zihao Li and Jianfeng Li and has published in prestigious journals such as IEEE Transactions on Software Engineering, Natural Hazards and ACM Transactions on Software Engineering and Methodology.

In The Last Decade

He Ye

8 papers receiving 245 citations

Hit Papers

MemoryBank: Enhancing Large Language Models with Long-Ter... 2024 2026 2025 2024 10 20 30 40 50

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
He Ye Sweden 6 129 127 58 31 19 12 245
Haoye Tian Luxembourg 8 138 1.1× 114 0.9× 34 0.6× 33 1.1× 29 1.5× 19 187
Martin Briesch Germany 4 102 0.8× 67 0.5× 109 1.9× 25 0.8× 22 1.2× 9 245
Davin McCall United Kingdom 6 125 1.0× 108 0.9× 29 0.5× 16 0.5× 7 0.4× 8 265
Rawad Abou Assi Lebanon 8 202 1.6× 190 1.5× 144 2.5× 51 1.6× 18 0.9× 16 348
Phil Greenwood United Kingdom 11 173 1.3× 31 0.2× 157 2.7× 92 3.0× 13 0.7× 28 283
Riku Saikkonen Finland 6 121 0.9× 114 0.9× 43 0.7× 12 0.4× 7 0.4× 12 290
Sávio Freire Brazil 10 208 1.6× 50 0.4× 46 0.8× 15 0.5× 6 0.3× 59 288
Raúl Mazo France 6 122 0.9× 31 0.2× 86 1.5× 39 1.3× 18 0.9× 23 153
Gias Uddin Canada 9 269 2.1× 44 0.3× 123 2.1× 81 2.6× 32 1.7× 10 306
Sonia Haiduc United States 12 259 2.0× 69 0.5× 102 1.8× 33 1.1× 36 1.9× 20 339

Countries citing papers authored by He Ye

Since Specialization
Citations

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

Fields of papers citing papers by He Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of He Ye

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

All Works

12 of 12 papers shown
1.
Keung, Jacky, He Ye, Claire Le Goues, et al.. (2025). When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair. ACM Transactions on Software Engineering and Methodology. 35(3). 1–46.
2.
Ye, He, et al.. (2025). AdverIntent-Agent: Adversarial Reasoning for Repair Based on Inferred Program Intent. Proceedings of the ACM on software engineering.. 2(ISSTA). 1398–1420.
4.
Zhong, Wanjun, et al.. (2024). MemoryBank: Enhancing Large Language Models with Long-Term Memory. Proceedings of the AAAI Conference on Artificial Intelligence. 38(17). 19724–19731. 53 indexed citations breakdown →
5.
Ye, He & Martin Monperrus. (2024). ITER: Iterative Neural Repair for Multi-Location Patches. 1–13. 23 indexed citations
6.
Li, Zihao, et al.. (2023). DeepInfer: Deep Type Inference from Smart Contract Bytecode. 745–757. 8 indexed citations
7.
Ye, He, Matías Martínez, & Martin Monperrus. (2022). Neural program repair with execution-based backpropagation. arXiv (Cornell University). 1506–1518. 96 indexed citations
8.
Ye, He, Jian Gu, Matías Martínez, Thomas Durieux, & Martin Monperrus. (2021). Automated Classification of Overfitting Patches With Statically Extracted Code Features. IEEE Transactions on Software Engineering. 48(8). 2920–2938. 43 indexed citations
9.
Ye, He, et al.. (2017). Analysis of social vulnerability of residential community to hazards in Tianjin, China. Natural Hazards. 87(2). 1223–1243. 17 indexed citations
10.
Ye, He. (2005). An Efficient and Secure Dynamic Group Signature Scheme.
11.
Ye, He. (2004). An Efficient and Secure Group Signature Scheme. 1 indexed citations
12.
Levin, Barbara B., Holly Robbins, & He Ye. (2004). Comparative Study of Synchronous and Asynchronous Online Case Discussions. Society for Information Technology & Teacher Education International Conference. 2004(1). 551–558. 4 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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