D-Index & Metrics Best Publications

Sunghun Kim

Hong Kong University of Science and Technology
China

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 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

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