Moshi Wei
Impact in
- Software top 2%
- Software Testing and Debugging Techniques
- Software Reliability and Analysis Research
- Information Systems top 5%
- Software Engineering Research
Papers in
-
- Software Engineering Research 9
-
- Adversarial Robustness in Machine Learning 5
- Text and Document Classification Technologies 2
- Machine Learning and Data Classification 2
- Topic Modeling 2
- Co-authors
- Lin Tan (1 shared paper)Thibaud Lutellier (1 shared paper)Hung Viet Pham (1 shared paper)Yitong Li (1 shared paper)Junjie Wang (7 shared papers)Yuchao Huang (4 shared papers)Song Wang (1 shared paper)Nachiappan Nagappan (1 shared paper)
- Journals
- ACM Transactions on Software Engineering and Methodology (4 papers)IEEE Transactions on Reliability (1 paper)IEEE Transactions on Software Engineering (1 paper)Information and Software Technology (1 paper)
- Partner nations
- CanadaChinaUnited States
In The Last Decade
Moshi Wei
12 papers receiving 299 citations
Moshi Wei's Hit Papers
Peers
Comparison fields: 5 of 24
- Software 200
- Information Systems 220
- Signal Processing 53
- Artificial Intelligence 90
- Computer Networks and Communications 62
Countries citing papers authored by Moshi Wei
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
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-authors
The 19 scholars most cited alongside Moshi Wei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | CoCoNuT: combining context-aware neural translation models using ensemble for program repair Hit paper breakdown → | 2020 | 211 |
| 2 | 2022 | 27 | |
| 3 | 2021 | 24 | |
| 4 | 2022 | 8 | |
| 5 | 2023 | 7 | |
| 6 | 2024 | 6 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2022 | 3 | |
| 11 | 2023 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2025 | 0 | |
| 14 | 2024 | 0 |
About Moshi Wei
Moshi Wei is a scholar working on Information Systems, Artificial Intelligence, Software, Signal Processing and Electrical and Electronic Engineering, having authored 14 papers that have together received 302 indexed citations. Recurring topics across this work include Software Engineering Research (9 papers), Software Testing and Debugging Techniques (6 papers), Adversarial Robustness in Machine Learning (5 papers), Advanced Malware Detection Techniques (3 papers), Software Reliability and Analysis Research (2 papers), Text and Document Classification Technologies (2 papers), Machine Learning and Data Classification (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Software (200 citations), Information Systems (220 citations), Signal Processing (53 citations), Artificial Intelligence (90 citations) and Computer Networks and Communications (62 citations). Moshi Wei has collaborated with scholars based in Canada, China and United States. Frequent 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. Their work appears in journals such as ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Reliability, IEEE Transactions on Software Engineering and Information and Software Technology.
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.