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 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.
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.
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.
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)
Classifying Software Changes: Clean or Buggy?
Sunghun Kim;E.J. Whitehead;Yi Zhang.
IEEE Transactions on Software Engineering (2008)
Predicting Faults from Cached History
Sunghun Kim;T. Zimmermann;E.J. Whitehead;A. Zeller.
international conference on software engineering (2007)
Automatic patch generation learned from human-written patches
Dongsun Kim;Jaechang Nam;Jaewoo Song;Sunghun Kim.
international conference on software engineering (2013)
Improving bug triage with bug tossing graphs
Gaeul Jeong;Sunghun Kim;Thomas Zimmermann.
foundations of software engineering (2009)
Automatically patching errors in deployed software
Jeff H. Perkins;Sunghun Kim;Sam Larsen;Saman Amarasinghe.
symposium on operating systems principles (2009)
Deep API learning
Xiaodong Gu;Hongyu Zhang;Dongmei Zhang;Sunghun Kim.
foundations of software engineering (2016)
Transfer defect learning
Jaechang Nam;Sinno Jialin Pan;Sunghun Kim.
international conference on software engineering (2013)
ReLink: recovering links between bugs and changes
Rongxin Wu;Hongyu Zhang;Sunghun Kim;Shing-Chi Cheung.
foundations of software engineering (2011)
Heterogeneous Defect Prediction
Jaechang Nam;Wei Fu;Sunghun Kim;Tim Menzies.
IEEE Transactions on Software Engineering (2018)
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