Junda He

523 total citations · 1 hit paper
20 papers, 227 citations indexed

About

Junda He is a scholar working on Information Systems, Artificial Intelligence and Software. According to data from OpenAlex, Junda He has authored 20 papers receiving a total of 227 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Information Systems, 11 papers in Artificial Intelligence and 4 papers in Software. Recurrent topics in Junda He's work include Software Engineering Research (10 papers), Topic Modeling (5 papers) and Natural Language Processing Techniques (4 papers). Junda He is often cited by papers focused on Software Engineering Research (10 papers), Topic Modeling (5 papers) and Natural Language Processing Techniques (4 papers). Junda He collaborates with scholars based in Singapore, United States and United Kingdom. Junda He's co-authors include David Lo, Zhou Yang, Jieke Shi, Bowen Xu, Christoph Treude, Xin Zhou, Hong Jin Kang, Kisub Kim, Jie M. Zhang and DongGyun Han and has published in prestigious journals such as IEEE Transactions on Software Engineering, Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology.

In The Last Decade

Junda He

17 papers receiving 226 citations

Hit Papers

LLM-Based Multi-Agent Systems for Software Engineering: L... 2025 2026 2025 5 10 15 20
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. LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead
    2025 24 citations Junda He, Christoph Treude 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
Junda He Singapore 8 131 121 64 51 39 20 227
Chenyang Lyu China 8 162 1.2× 94 0.8× 96 1.5× 88 1.7× 43 1.1× 26 291
Rafael S. Durelli Brazil 8 70 0.5× 169 1.4× 129 2.0× 23 0.5× 51 1.3× 32 224
Tejeddine Mouelhi Luxembourg 12 122 0.9× 143 1.2× 187 2.9× 47 0.9× 88 2.3× 20 315
Adwait Nadkarni United States 11 145 1.1× 167 1.4× 66 1.0× 225 4.4× 103 2.6× 19 300
Jiazhen Gu China 8 88 0.7× 77 0.6× 53 0.8× 16 0.3× 100 2.6× 23 210
Ting Dai United States 10 148 1.1× 99 0.8× 56 0.9× 51 1.0× 115 2.9× 25 295
Rishita Anubhai United States 4 177 1.4× 172 1.4× 52 0.8× 170 3.3× 82 2.1× 6 322
Paria Shirani Canada 7 101 0.8× 120 1.0× 46 0.7× 150 2.9× 79 2.0× 10 206
Nilo Redini United States 9 116 0.9× 104 0.9× 82 1.3× 200 3.9× 96 2.5× 13 256
Karel Frajták Czechia 9 58 0.4× 131 1.1× 57 0.9× 18 0.4× 143 3.7× 21 209

Countries citing papers authored by Junda He

Since Specialization
Citations

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

Fields of papers citing papers by Junda He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junda He

This figure shows the co-authorship network connecting the top 25 collaborators of Junda He. A scholar is included among the top collaborators of Junda He 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 Junda He. Junda He 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.
He, Junda, et al.. (2025). Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks. ACM Transactions on Software Engineering and Methodology. 1 indexed citations
2.
3.
Shi, Jieke, et al.. (2025). Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies. ACM Transactions on Software Engineering and Methodology.
4.
He, Junda, Christoph Treude, & David Lo. (2025). LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead. ACM Transactions on Software Engineering and Methodology. 34(5). 1–30. 24 indexed citations breakdown →
5.
Shi, Jieke, Zhou Yang, Junda He, et al.. (2025). Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs. ACM Transactions on Software Engineering and Methodology. 2 indexed citations
6.
Liu, Zekun, Mengxing Guo, Junda He, & Ateekh Ur Rehman. (2025). On the electro-vibrations of piezoelectric structure rested on concrete auxetic foundation via Carrera unified formulation validated by machine learning algorithm. Mechanics of Advanced Materials and Structures. 33(1). 2 indexed citations
7.
Zhou, Xin, Bowen Xu, Kisub Kim, et al.. (2024). Leveraging Large Language Model for Automatic Patch Correctness Assessment. IEEE Transactions on Software Engineering. 50(11). 2865–2883. 3 indexed citations
8.
Gong, Chen, Zhou Yang, Yunpeng Bai, et al.. (2024). Baffle: Hiding Backdoors in Offline Reinforcement Learning Datasets. 2086–2104. 4 indexed citations
9.
He, Junda, Zhou Yang, Jieke Shi, et al.. (2024). Curiosity-Driven Testing for Sequential Decision-Making Process. 1–14. 3 indexed citations
10.
Shi, Jieke, Zhou Yang, Hong Jin Kang, et al.. (2024). Greening Large Language Models of Code. 142–153. 14 indexed citations
11.
He, Junda, Bowen Xu, Zhou Yang, et al.. (2024). PTM4Tag+: Tag recommendation of stack overflow posts with pre-trained models. Empirical Software Engineering. 30(1).
12.
Yang, Zhou, Bowen Xu, Jie M. Zhang, et al.. (2024). Stealthy Backdoor Attack for Code Models. IEEE Transactions on Software Engineering. 50(4). 721–741. 27 indexed citations
13.
Zhou, Xin, Bowen Xu, DongGyun Han, et al.. (2023). CCBERT: Self-Supervised Code Change Representation Learning. 182–193. 6 indexed citations
14.
He, Junda, Xin Zhou, Bowen Xu, et al.. (2023). Representation Learning for Stack Overflow Posts: How Far Are We?. ACM Transactions on Software Engineering and Methodology. 33(3). 1–24. 14 indexed citations
15.
Zhou, Xin, Kisub Kim, Bowen Xu, et al.. (2023). Generation-based Code Review Automation: How Far Are We?. 215–226. 11 indexed citations
16.
Fadda, Edoardo, Junda He, Claudio J. Tessone, & Paolo Barucca. (2022). Consensus formation on heterogeneous networks. EPJ Data Science. 11(1). 5 indexed citations
17.
Yang, Zhou, Jieke Shi, Junda He, & David Lo. (2022). Natural attack for pre-trained models of code. arXiv (Cornell University). 1482–1493. 89 indexed citations
18.
Shi, Jieke, Zhou Yang, Junda He, Bowen Xu, & David Lo. (2022). Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?. Figshare. 1134–1138. 11 indexed citations
19.
Xu, Bowen, Ferdian Thung, Ting Zhang, et al.. (2022). Answer Summarization for Technical Queries: Benchmark and New Approach. 1–13. 8 indexed citations
20.
He, Junda, et al.. (2010). An Analysis of Users' Cognitive Factors Towards Icon in Interactive Interface. 26–28. 3 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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