A. Svyatkovskiy
- Software top 2%
- Software Testing and Debugging Techniques 6
- Software Reliability and Analysis Research 3
- Information Systems top 2%
- Software Engineering Research 7
- Software Engineering Techniques and Practices 2
- Web Data Mining and Analysis 1
- Signal Processing top 5%
- Advanced Malware Detection Techniques 1
- Artificial Intelligence top 5%
- Topic Modeling 3
- Natural Language Processing Techniques 1
- Co-authors
- Neel SundaresanShuai LuMichele TufanoNan DuanDaya GuoSheng‐Yu FuColin B. ClementDawn Drain
- Journals
- IEEE Transactions on Software Engineering (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
A. Svyatkovskiy
8 papers receiving 468 citations
Hit Papers
Peers
Comparison fields: 5 of 35
- Software 196
- Information Systems 360
- Signal Processing 110
- Artificial Intelligence 211
- Health Informatics 8
Countries citing papers authored by A. Svyatkovskiy
This map shows the geographic impact of A. Svyatkovskiy'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 A. Svyatkovskiy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Svyatkovskiy more than expected).
Fields of papers citing papers by A. Svyatkovskiy
This network shows the impact of papers produced by A. Svyatkovskiy. 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 A. Svyatkovskiy. The network helps show where A. Svyatkovskiy may publish in the future.
Co-authorship network
The 25 scholars most cited alongside A. Svyatkovskiy, 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 | 2025 | 3 | |
| 2 | 2023 | 1 | |
| 3 | InferFix: End-to-End Program Repair with LLMsbreakdown → | 2023 | 76 |
| 4 | 2022 | 16 | |
| 5 | Automating code review activities by large-scale pre-trainingbreakdown → | 2022 | 92 |
| 6 | 2022 | 18 | |
| 7 | GraphCodeBERT: Pre-training Code Representations with Data Flowbreakdown → | 2021 | 261 |
| 8 | 2021 | 8 |
About A. Svyatkovskiy
A. Svyatkovskiy is a scholar working on Software, Information Systems and Artificial Intelligence, having authored 8 papers that have together received 475 indexed citations. Recurring topics across this work include Software Engineering Research (7 papers), Software Testing and Debugging Techniques (6 papers), Software Reliability and Analysis Research (3 papers), Topic Modeling (3 papers), Software Engineering Techniques and Practices (2 papers), Web Data Mining and Analysis (1 paper), Natural Language Processing Techniques (1 paper) and Advanced Malware Detection Techniques (1 paper). The work is most often cited by research in Software (196 citations), Information Systems (360 citations) and Signal Processing (110 citations). A. Svyatkovskiy has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Neel Sundaresan, Shuai Lu, Michele Tufano, Nan Duan, Daya Guo, Sheng‐Yu Fu, Colin B. Clement, Dawn Drain, Jian Yin and Ming Zhou. Their work appears in journals such as IEEE Transactions on Software Engineering, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.