Dawn Drain

2.7k citations
3 papers · 284 · 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
arXiv (Cornell University) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)

In The Last Decade

Dawn Drain

3 papers receiving 279 citations

Dawn Drain's Hit Papers

GraphCodeBERT: Pre-training Code Representations with Data Flow 2021 · 269 citations
2690+1+3Years since publication50100150200250

Peers

Dawn Drain
Comparison fields: 5 of 23
  • Software 105
  • Information Systems 224
  • Signal Processing 84
  • Artificial Intelligence 151
  • Computer Networks and Communications 57
Replace Cristian-Alexandru Staicu with:
Cristian-Alexandru Staicu Germany
Qiyi Tang China
Ensheng Shi China
Hung Phan United States
Huanting Wang China
Xujie Si United States
Quanjun Zhang China
Arman Shahbazian United States
Paria Shirani Canada
Moshi Wei Canada
Dawn Drain relative to Cristian-Alexandru Staicu Germany Cristian-Alexandru Staicu's profile →
Citations per field
00.5×2.9×
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 284 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (2 papers), Software Engineering Research (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 (105 citations), Information Systems (224 citations), Signal Processing (84 citations), Artificial Intelligence (151 citations) and Computer Networks and Communications (57 citations). Dawn Drain has collaborated with scholars based in United Kingdom, Germany and China. Frequent co-authors include Michele Tufano, Colin B. Clement, Neel Sundaresan, Shuai Lu, A. Svyatkovskiy, Nan Duan, Daxin Jiang, Shujie Liu, Long Zhou and Duyu Tang. Their work appears in journals such as 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.

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