What is he best known for?
The fields of study he is best known for:
- Software
- Operating system
- Programming language
Ying Zou mostly deals with Data mining, Software, Software quality, Software engineering and Software metric.
His research in Data mining intersects with topics in Machine learning, Random forest, Artificial intelligence and Regression testing.
As part of his studies on Software, Ying Zou frequently links adjacent subjects like Code.
His studies deal with areas such as Software bug and Software system as well as Software quality.
His multidisciplinary approach integrates Software engineering and Empirical research in his work.
His biological study spans a wide range of topics, including Rank and Set.
His most cited work include:
- Methods for Generating Auxiliary Data Operations for a Role Based Personalized Business User Workplace (149 citations)
- Cross-project defect prediction using a connectivity-based unsupervised classifier (123 citations)
- Spotting working code examples (104 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in World Wide Web, Software engineering, Web service, Empirical research and Software.
His research integrates issues of Service delivery framework and Differentiated service in his study of World Wide Web.
His work carried out in the field of Software engineering brings together such families of science as Artifact-centric business process model, Business process modeling, Business rule, Source code and Software quality.
His Software research includes themes of Rank, Data mining and Code.
His Data mining study combines topics from a wide range of disciplines, such as Software metric, Software regression, Software performance testing, Artificial intelligence and Machine learning.
His work in Code addresses issues such as Database, which are connected to fields such as Set and Quality.
He most often published in these fields:
- World Wide Web (27.67%)
- Software engineering (24.53%)
- Web service (22.64%)
What were the highlights of his more recent work (between 2016-2021)?
- Empirical research (18.87%)
- World Wide Web (27.67%)
- Android (6.92%)
In recent papers he was focusing on the following fields of study:
Empirical research, World Wide Web, Android, Code and Software are his primary areas of study.
His World Wide Web research includes elements of Thread, Documentation and Stack overflow.
In Code, he works on issues like Information retrieval, which are connected to Rank, Source code and Domain knowledge.
Ying Zou studies Software, namely Software metric.
His work focuses on many connections between Process and other disciplines, such as Software system, that overlap with his field of interest in Database.
His study looks at the relationship between Software development and fields such as Application programming interface, as well as how they intersect with chemical problems.
Between 2016 and 2021, his most popular works were:
- Learning to rank code examples for code search engines (44 citations)
- The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models (43 citations)
- Towards just-in-time suggestions for log changes (39 citations)
In his most recent research, the most cited papers focused on:
- Software
- Operating system
- Programming language
His scientific interests lie mostly in Empirical research, Android, Software, World Wide Web and Source code.
In the subject of general Software, his work in Cyclomatic complexity is often linked to Order and Set, thereby combining diverse domains of study.
His World Wide Web research incorporates themes from Thread, Stack overflow and Knowledge engineering.
His work deals with themes such as Learning to rank, Information retrieval, Search engine, Database and Code, which intersect with Source code.
The concepts of his Database study are interwoven with issues in Software system and Domain knowledge.
His Software development research focuses on subjects like Application programming interface, which are linked to Data mining.
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