Dawn Drain

2.3k citations
3 papers · 275 · 1 hit paper · h-index 3

Impact in

  • Software top 5%
    • Software Testing and Debugging Techniques
    • Software Reliability and Analysis Research
    • Software Engineering Research

Papers in

Journals
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)arXiv (Cornell University) (1 paper)

In The Last Decade

Dawn Drain

3 papers receiving 268 citations

Dawn Drain's Hit Papers

GraphCodeBERT: Pre-training Code Representations with Data Flow 2021 · 261 citations
2610+1+3Years since publication50100150200250

Peers

Dawn Drain
Comparison fields: 5 of 22
  • Software 99
  • Information Systems 218
  • Signal Processing 82
  • Artificial Intelligence 150
  • Computer Networks and Communications 56
Replace Cristian-Alexandru Staicu with:
Cristian-Alexandru Staicu Germany
Johannes Späth Germany
Ensheng Shi China
Kisub Kim Singapore
Hung Phan United States
Qiyi Tang China
Vijayaraghavan Murali United States
Arman Shahbazian United States
Quanjun Zhang China
Michael J. Decker United States
Dawn Drain relative to Cristian-Alexandru Staicu Germany Cristian-Alexandru Staicu's profile →
Citations per field
00.5×2.8×
Cristian-Alexandru Staicu · 1×
Citations per year

Countries citing papers authored by Dawn Drain

Since Specialization
Citations

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

Fields of papers citing papers by Dawn Drain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Dawn Drain, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dawn Drain Line = papers co-authored together Dawn Drain links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown

About Dawn Drain

Dawn Drain is a scholar working on Information Systems, Artificial Intelligence, Software, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 275 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Software Engineering Research (2 papers), Natural Language Processing Techniques (2 papers), Web Data Mining and Analysis (1 paper) and Software Testing and Debugging Techniques (1 paper). The work is most often cited by research in Software (99 citations), Information Systems (218 citations), Signal Processing (82 citations), Artificial Intelligence (150 citations) and Computer Networks and Communications (56 citations). Dawn Drain has collaborated with scholars based in United Kingdom, Canada and Germany. Frequent co-authors include Michele Tufano, Neel Sundaresan, Colin B. Clement, A. Svyatkovskiy, Nan Duan, Shuai Lu, Daya Guo, Shao Kun Deng, Sheng‐Yu Fu and Shuo Ren. Their work appears in journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and arXiv (Cornell University).

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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