His main research concerns Data mining, Artificial intelligence, Software, Source code and Software development. The study incorporates disciplines such as Workload, Inference, Profiling and Time series in addition to Data mining. His Artificial intelligence research incorporates elements of XML and Computer vision.
Dongmei Zhang has researched Source code in several fields, including Code review, KPI-driven code analysis and Code generation. The concepts of his Software development study are interwoven with issues in Microsoft Visual Studio and Scalability. Dongmei Zhang combines subjects such as Language model, Parsing, Natural language user interface, Word and Information retrieval with his study of Deep learning.
The scientist’s investigation covers issues in Artificial intelligence, Data mining, Software, Natural language and Service. His research on Artificial intelligence focuses in particular on Parsing. Dongmei Zhang has included themes like Cluster analysis and Data set in his Data mining study.
Dongmei Zhang works mostly in the field of Software, limiting it down to concerns involving Software engineering and, occasionally, Software analytics. Dongmei Zhang interconnects Query analysis and Information retrieval in the investigation of issues within Natural language. His Software development study combines topics in areas such as Software deployment, Data science and Source code.
Artificial intelligence, Natural language processing, Natural language, Parsing and Information retrieval are his primary areas of study. In Artificial intelligence, he works on issues like Machine learning, which are connected to Human intelligence. His Automatic summarization study, which is part of a larger body of work in Natural language processing, is frequently linked to Schema, bridging the gap between disciplines.
His Natural language research includes themes of Query analysis and Usability. His study in Parsing is interdisciplinary in nature, drawing from both SQL and Code. In his study, Data mining, Incident management and Identification is strongly linked to Service, which falls under the umbrella field of Deep learning.
His scientific interests lie mostly in Artificial intelligence, Data mining, Human–computer interaction, Natural language processing and Natural language. His study connects Generalization and Artificial intelligence. His Data mining study incorporates themes from Active learning and Focus.
In general Human–computer interaction study, his work on Persona often relates to the realm of Focus, Conversation and Perception, thereby connecting several areas of interest. His research integrates issues of SQL and Leverage in his study of Natural language processing. Dongmei Zhang has researched SQL in several fields, including Parsing, Word embedding, Utterance, Semantics and Semantic relationship.
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Deep API learning
Xiaodong Gu;Hongyu Zhang;Dongmei Zhang;Sunghun Kim.
foundations of software engineering (2016)
Harmonic maps and their applications in surface matching
Dongmei Zhang;M. Hebert.
computer vision and pattern recognition (1999)
Mining succinct and high-coverage API usage patterns from source code
Jue Wang;Yingnong Dang;Hongyu Zhang;Kai Chen.
mining software repositories (2013)
Where do developers log? an empirical study on logging practices in industry
Qiang Fu;Jieming Zhu;Wenlu Hu;Jian-Guang Lou.
international conference on software engineering (2014)
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Jiaqi Guo;Zecheng Zhan;Yan Gao;Yan Xiao.
meeting of the association for computational linguistics (2019)
How do software engineers understand code changes?: an exploratory study in industry
Yida Tao;Yingnong Dang;Tao Xie;Dongmei Zhang.
foundations of software engineering (2012)
CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E)
Fei Lv;Hongyu Zhang;Jian-guang Lou;Shaowei Wang.
automated software engineering (2015)
Learning to log: helping developers make informed logging decisions
Jieming Zhu;Pinjia He;Qiang Fu;Hongyu Zhang.
international conference on software engineering (2015)
ReBucket: a method for clustering duplicate crash reports based on call stack similarity
Yingnong Dang;Rongxin Wu;Hongyu Zhang;Dongmei Zhang.
international conference on software engineering (2012)
Robust log-based anomaly detection on unstable log data
Xu Zhang;Yong Xu;Qingwei Lin;Bo Qiao.
foundations of software engineering (2019)
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