D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 56 Citations 14,636 117 World Ranking 2661 National Ranking 263

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Programming language
  • Software

The scientist’s investigation covers issues in Software bug, Data mining, Source code, Artificial intelligence and Software. His research integrates issues of Consistency, Software maintenance, Syntax and Public domain software in his study of Software bug. He studied Data mining and Data quality that intersect with Heuristics, Maintainability and Prediction algorithms.

His work deals with themes such as Outlier, Process, Natural language and Identification, which intersect with Source code. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Semantics and Information retrieval. His Software study incorporates themes from Learning classifier system, Real-time computing, Exploit, Control flow and Debugging.

His most cited work include:

  • StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation (1418 citations)
  • Classifying Software Changes: Clean or Buggy? (454 citations)
  • Automatic patch generation learned from human-written patches (417 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Software, Source code, Data mining, Artificial intelligence and Software bug. His Software study combines topics from a wide range of disciplines, such as Java, World Wide Web, Software engineering and Crash. His research in Source code intersects with topics in Database, KPI-driven code analysis, Static program analysis and Code generation, Code.

Sunghun Kim has included themes like Software regression and Software metric in his Data mining study. Sunghun Kim has researched Artificial intelligence in several fields, including Machine learning and Natural language processing. Sunghun Kim usually deals with Software bug and limits it to topics linked to Software maintenance and Software evolution.

He most often published in these fields:

  • Software (35.43%)
  • Source code (24.41%)
  • Data mining (22.05%)

What were the highlights of his more recent work (between 2016-2020)?

  • Artificial intelligence (20.47%)
  • Code (9.45%)
  • Source code (24.41%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Code, Source code, Machine learning and Domain. His work on Deep learning, Semantics and Embedding as part of general Artificial intelligence study is frequently linked to Matching and Original meaning, bridging the gap between disciplines. The Source code study combines topics in areas such as Redundancy and Reservation.

His studies in Machine learning integrate themes in fields like Visualization, SIGNAL and Focus. The concepts of his Domain study are interwoven with issues in Image, Translation, Image translation, Facial expression and Robustness. Software metric is a subfield of Software that Sunghun Kim tackles.

Between 2016 and 2020, his most popular works were:

  • StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation (1418 citations)
  • Deep code search (190 citations)
  • Heterogeneous Defect Prediction (106 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Yunjey Choi;Minje Choi;Munyoung Kim;Jung-Woo Ha.
computer vision and pattern recognition (2018)

2313 Citations

Classifying Software Changes: Clean or Buggy?

Sunghun Kim;E.J. Whitehead;Yi Zhang.
IEEE Transactions on Software Engineering (2008)

718 Citations

Predicting Faults from Cached History

Sunghun Kim;T. Zimmermann;E.J. Whitehead;A. Zeller.
international conference on software engineering (2007)

642 Citations

Automatic patch generation learned from human-written patches

Dongsun Kim;Jaechang Nam;Jaewoo Song;Sunghun Kim.
international conference on software engineering (2013)

629 Citations

Improving bug triage with bug tossing graphs

Gaeul Jeong;Sunghun Kim;Thomas Zimmermann.
foundations of software engineering (2009)

578 Citations

Automatically patching errors in deployed software

Jeff H. Perkins;Sunghun Kim;Sam Larsen;Saman Amarasinghe.
symposium on operating systems principles (2009)

489 Citations

Deep API learning

Xiaodong Gu;Hongyu Zhang;Dongmei Zhang;Sunghun Kim.
foundations of software engineering (2016)

466 Citations

Transfer defect learning

Jaechang Nam;Sinno Jialin Pan;Sunghun Kim.
international conference on software engineering (2013)

451 Citations

ReLink: recovering links between bugs and changes

Rongxin Wu;Hongyu Zhang;Sunghun Kim;Shing-Chi Cheung.
foundations of software engineering (2011)

384 Citations

Heterogeneous Defect Prediction

Jaechang Nam;Wei Fu;Sunghun Kim;Tim Menzies.
IEEE Transactions on Software Engineering (2018)

382 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Sunghun Kim

David Lo

David Lo

Singapore Management University

Publications: 159

Xin Xia

Xin Xia

Huawei Technologies (China)

Publications: 97

Ahmed E. Hassan

Ahmed E. Hassan

Queen's University

Publications: 73

Martin Monperrus

Martin Monperrus

KTH Royal Institute of Technology

Publications: 64

Kenichi Matsumoto

Kenichi Matsumoto

Nara Institute of Science and Technology

Publications: 48

Hongyu Zhang

Hongyu Zhang

University of Newcastle Australia

Publications: 47

Denys Poshyvanyk

Denys Poshyvanyk

William & Mary

Publications: 44

Harald C. Gall

Harald C. Gall

University of Zurich

Publications: 44

Westley Weimer

Westley Weimer

University of Michigan–Ann Arbor

Publications: 41

Tim Menzies

Tim Menzies

North Carolina State University

Publications: 41

Tien N. Nguyen

Tien N. Nguyen

The University of Texas at Dallas

Publications: 41

Gabriele Bavota

Gabriele Bavota

Universita della Svizzera Italiana

Publications: 39

Tegawendé F. Bissyandé

Tegawendé F. Bissyandé

University of Luxembourg

Publications: 39

Nicu Sebe

Nicu Sebe

University of Trento

Publications: 34

Martin Rinard

Martin Rinard

MIT

Publications: 34

Bram Adams

Bram Adams

Queen's University

Publications: 33

Trending Scientists

Murray Shanahan

Murray Shanahan

Imperial College London

Dayal R. Parhi

Dayal R. Parhi

National Institute of Technology Rourkela

Hans Peter Lang

Hans Peter Lang

University of Basel

Byeong-Soo Bae

Byeong-Soo Bae

Korea Advanced Institute of Science and Technology

Francesco Salamini

Francesco Salamini

Parco Tecnologico Padano

Nikolay V. Dokholyan

Nikolay V. Dokholyan

Pennsylvania State University

Janet Hall

Janet Hall

National Institute of Environmental Health Sciences

Ian Parker

Ian Parker

University of California, Irvine

Kaarle Hämeri

Kaarle Hämeri

University of Helsinki

A. Scott Denning

A. Scott Denning

Colorado State University

Andrew J. Friedland

Andrew J. Friedland

Dartmouth College

Gunter Schumann

Gunter Schumann

King's College London

Harry R. Keiser

Harry R. Keiser

National Institutes of Health

Philip B. McGlave

Philip B. McGlave

University of Minnesota

Ruth Meinzen-Dick

Ruth Meinzen-Dick

International Food Policy Research Institute

Matthew Gabel

Matthew Gabel

Washington University in St. Louis

Something went wrong. Please try again later.