2020 - ACM Distinguished Member
Hongyu Zhang focuses on Data mining, Source code, Software bug, Database and Code. His biological study spans a wide range of topics, including Intersection and Software quality. His Software bug study integrates concerns from other disciplines, such as Data quality, Mining software repositories and Measure.
His biological study spans a wide range of topics, including Software system, Software and Cluster analysis. His Code study integrates concerns from other disciplines, such as Search problem and Natural language, Artificial intelligence, Benchmark. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Information retrieval and XML.
Data mining, Software, Software engineering, Artificial intelligence and Source code are his primary areas of study. His research on Data mining also deals with topics like
His Software engineering study combines topics from a wide range of disciplines, such as Programming language, Debugging and XML. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Natural language processing. His study in Source code is interdisciplinary in nature, drawing from both Software regression, Database and Code.
Hongyu Zhang mainly investigates Artificial intelligence, Software, Code, Deep learning and Data mining. His study in the field of Software system is also linked to topics like Work. Hongyu Zhang has researched Software system in several fields, including Software bug, Performance prediction, Speedup and Task.
His studies deal with areas such as Java, Artificial neural network, Set and Natural language as well as Code. The various areas that he examines in his Natural language study include Semantics and Source code. His work in the fields of Data mining, such as Anomaly detection, overlaps with other areas such as Input reduction.
His main research concerns Artificial intelligence, Artificial neural network, Software, Empirical research and Data mining. His Artificial intelligence study frequently draws connections to adjacent fields such as Code. His Code study incorporates themes from Source code and Natural language, Natural language processing.
His research in Software intersects with topics in Correctness, Oracle and Obfuscation. His Empirical research research integrates issues from Service system, Incident management and Incident management. His Data mining research incorporates themes from Performance prediction, Baseline and Feedforward neural network.
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.
Where should the bugs be fixed? - more accurate information retrieval-based bug localization based on bug reports
Jian Zhou;Hongyu Zhang;David Lo.
international conference on software engineering (2012)
Deep API learning
Xiaodong Gu;Hongyu Zhang;Dongmei Zhang;Sunghun Kim.
foundations of software engineering (2016)
ReLink: recovering links between bugs and changes
Rongxin Wu;Hongyu Zhang;Sunghun Kim;Shing-Chi Cheung.
foundations of software engineering (2011)
Dealing with noise in defect prediction
Sunghun Kim;Hongyu Zhang;Rongxin Wu;Liang Gong.
international conference on software engineering (2011)
Deep code search
Xiaodong Gu;Hongyu Zhang;Sunghun Kim.
international conference on software engineering (2018)
Log clustering based problem identification for online service systems
Qingwei Lin;Hongyu Zhang;Jian-Guang Lou;Yu Zhang.
international conference on software engineering (2016)
A novel neural source code representation based on abstract syntax tree
Jian Zhang;Xu Wang;Hongyu Zhang;Hailong Sun.
international conference on software engineering (2019)
Formal semantics and verification for feature modeling
Jing Sun;Hongyu Zhang;Yuan Fang;Li Hai Wang.
international conference on engineering of complex computer systems (2005)
Mining succinct and high-coverage API usage patterns from source code
Jue Wang;Yingnong Dang;Hongyu Zhang;Kai Chen.
mining software repositories (2013)
Sample-based software defect prediction with active and semi-supervised learning
Ming Li;Hongyu Zhang;Rongxin Wu;Zhi-Hua Zhou.
automated software engineering (2012)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: