A. Svyatkovskiy

15.1k total citations · 3 hit papers
8 papers, 475 citations indexed

About

A. Svyatkovskiy is a scholar working on Information Systems, Software and Artificial Intelligence. According to data from OpenAlex, A. Svyatkovskiy has authored 8 papers receiving a total of 475 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Information Systems, 7 papers in Software and 3 papers in Artificial Intelligence. Recurrent topics in A. Svyatkovskiy's work include Software Engineering Research (7 papers), Software Testing and Debugging Techniques (6 papers) and Software Reliability and Analysis Research (3 papers). A. Svyatkovskiy is often cited by papers focused on Software Engineering Research (7 papers), Software Testing and Debugging Techniques (6 papers) and Software Reliability and Analysis Research (3 papers). A. Svyatkovskiy collaborates with scholars based in United States, United Kingdom and China. A. Svyatkovskiy's 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 and has published in prestigious journals such as IEEE Transactions on Software Engineering, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

In The Last Decade

A. Svyatkovskiy

8 papers receiving 468 citations

Hit Papers

GraphCodeBERT: Pre-training Code Representations with Dat... 2021 2026 2022 2024 2021 2022 2023 50 100 150 200 250
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. GraphCodeBERT: Pre-training Code Representations with Data Flow
    2021 261 citations Daya Guo, Shuo Ren et al. profile →
  2. Automating code review activities by large-scale pre-training
    2022 92 citations Zhiyu Li, Shuai Lu et al. profile →
  3. InferFix: End-to-End Program Repair with LLMs
    2023 76 citations Michele Tufano, Shuai Lu et al. profile →

Peers

A. Svyatkovskiy
Shangqing Liu Singapore
Van Nguyen Australia
Francisco Servant United States
Zhongxin Liu China
Maria Christakis Germany
Davood Mazinanian Canada
Hong Jin Kang Singapore
Katsuhisa Maruyama Japan
Yoann Padioleau Denmark
Sæmundur Ó. Haraldsson United Kingdom
Shangqing Liu Singapore
A. Svyatkovskiy
Citations per year, relative to A. Svyatkovskiy A. Svyatkovskiy (= 1×) peers Shangqing Liu

Countries citing papers authored by A. Svyatkovskiy

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of A. Svyatkovskiy

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

All Works

8 of 8 papers shown
1.
Tufano, Michele, et al.. (2025). Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation. 37–44. 3 indexed citations
2.
Sundaresan, Neel, et al.. (2023). AdaptivePaste: Intelligent Copy-Paste in IDE. 1844–1854. 1 indexed citations
3.
Tufano, Michele, et al.. (2023). InferFix: End-to-End Program Repair with LLMs. 1646–1656. 76 indexed citations breakdown →
4.
Svyatkovskiy, A., Sarah Fakhoury, Todd Mytkowicz, et al.. (2022). Program merge conflict resolution via neural transformers. arXiv (Cornell University). 822–833. 16 indexed citations
5.
Li, Zhiyu, Shuai Lu, Daya Guo, et al.. (2022). Automating code review activities by large-scale pre-training. 1035–1047. 92 indexed citations breakdown →
6.
Mytkowicz, Todd, et al.. (2022). DeepMerge: Learning to Merge Programs. IEEE Transactions on Software Engineering. 49(4). 1599–1614. 18 indexed citations
7.
Guo, Daya, Shuo Ren, Shuai Lu, et al.. (2021). GraphCodeBERT: Pre-training Code Representations with Data Flow. 261 indexed citations breakdown →
8.
Clement, Colin B., Shuai Lu, Xiaoyu Liu, et al.. (2021). Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 4713–4722. 8 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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