Moshi Wei

500 total citations · 1 hit paper
14 papers, 302 citations indexed

About

Moshi Wei is a scholar working on Information Systems, Artificial Intelligence and Software. According to data from OpenAlex, Moshi Wei has authored 14 papers receiving a total of 302 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Information Systems, 10 papers in Artificial Intelligence and 6 papers in Software. Recurrent topics in Moshi Wei's work include Software Engineering Research (9 papers), Software Testing and Debugging Techniques (6 papers) and Adversarial Robustness in Machine Learning (5 papers). Moshi Wei is often cited by papers focused on Software Engineering Research (9 papers), Software Testing and Debugging Techniques (6 papers) and Adversarial Robustness in Machine Learning (5 papers). Moshi Wei collaborates with scholars based in Canada, China and United States. Moshi Wei's co-authors include Lin Tan, Thibaud Lutellier, Hung Viet Pham, Yitong Li, Junjie Wang, Yuchao Huang, Song Wang, Song Wang, Nachiappan Nagappan and Song Wang and has published in prestigious journals such as IEEE Transactions on Software Engineering, IEEE Transactions on Reliability and Information and Software Technology.

In The Last Decade

Moshi Wei

12 papers receiving 299 citations

Hit Papers

CoCoNuT: combining context-aware neural translation model... 2020 2026 2022 2024 2020 50 100 150 200
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. CoCoNuT: combining context-aware neural translation models using ensemble for program repair
    2020 211 citations Thibaud Lutellier, Hung Viet Pham et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Moshi Wei Canada 6 220 200 90 62 53 14 302
Alexandru Marginean United Kingdom 6 164 0.7× 192 1.0× 55 0.6× 51 0.8× 49 0.9× 7 254
Steve Kommrusch United States 3 253 1.1× 236 1.2× 70 0.8× 74 1.2× 62 1.2× 5 328
Vijayaraghavan Murali United States 10 162 0.7× 123 0.6× 79 0.9× 34 0.5× 33 0.6× 19 221
Kisub Kim Singapore 10 284 1.3× 166 0.8× 118 1.3× 68 1.1× 69 1.3× 28 353
Quanjun Zhang China 10 185 0.8× 179 0.9× 76 0.8× 52 0.8× 58 1.1× 37 303
Anil Koyuncu Luxembourg 8 358 1.6× 344 1.7× 59 0.7× 74 1.2× 80 1.5× 13 424
Michael J. Decker United States 9 296 1.3× 181 0.9× 102 1.1× 73 1.2× 86 1.6× 22 325
Aryaz Eghbali Germany 5 110 0.5× 107 0.5× 55 0.6× 40 0.6× 19 0.4× 7 190
Cristian-Alexandru Staicu Germany 9 246 1.1× 114 0.6× 138 1.5× 55 0.9× 182 3.4× 14 306
Hung Phan United States 8 263 1.2× 105 0.5× 101 1.1× 76 1.2× 92 1.7× 16 290

Countries citing papers authored by Moshi Wei

Since Specialization
Citations

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

Fields of papers citing papers by Moshi Wei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Moshi Wei

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

All Works

14 of 14 papers shown
1.
Wei, Moshi, et al.. (2025). Evaluating API-Level Deep Learning Fuzzers: A Comprehensive Benchmarking Study. ACM Transactions on Software Engineering and Methodology. 35(2). 1–34.
2.
Wei, Moshi, et al.. (2024). Effectiveness of ChatGPT for Static Analysis: How Far Are We?. 151–160. 4 indexed citations
3.
Wei, Moshi, et al.. (2024). Demystifying and Detecting Misuses of Deep Learning APIs. 1–12. 6 indexed citations
4.
Hemmati, Hadi, et al.. (2024). Assessing Evaluation Metrics for Neural Test Oracle Generation. IEEE Transactions on Software Engineering. 50(9). 2337–2349. 5 indexed citations
5.
Huang, Yue-Kai, Junjie Wang, Song Wang, et al.. (2024). Deep API Sequence Generation via Golden Solution Samples and API Seeds. ACM Transactions on Software Engineering and Methodology. 34(2). 1–21. 1 indexed citations
6.
Wei, Moshi, et al.. (2024). History-Driven Fuzzing for Deep Learning Libraries. ACM Transactions on Software Engineering and Methodology. 34(1). 1–29.
7.
Wei, Moshi, et al.. (2023). The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks. ACM Transactions on Software Engineering and Methodology. 33(2). 1–24. 7 indexed citations
8.
9.
Huang, Yuchao, Moshi Wei, Song Wang, Junjie Wang, & Qing Wang. (2022). Yet another combination of IR- and neural-based comment generation. Information and Software Technology. 152. 107001–107001. 4 indexed citations
10.
Wei, Moshi, et al.. (2022). API recommendation for machine learning libraries: how far are we?. 370–381. 3 indexed citations
11.
Wei, Moshi, et al.. (2022). CLEAR. 376–387. 27 indexed citations
12.
Wei, Moshi, Yuchao Huang, Jinqiu Yang, Junjie Wang, & Song Wang. (2022). CoCoFuzzing: Testing Neural Code Models With Coverage-Guided Fuzzing. IEEE Transactions on Reliability. 72(3). 1276–1289. 8 indexed citations
13.
Wang, Song, et al.. (2021). Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?. 1548–1560. 24 indexed citations
14.
Lutellier, Thibaud, et al.. (2020). CoCoNuT: combining context-aware neural translation models using ensemble for program repair. 101–114. 211 indexed citations breakdown →

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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