What is he best known for?
The fields of study he is best known for:
- Operating system
- Software
- Programming language
His primary scientific interests are in Software, Software engineering, Test case, Software architecture and Data mining.
His Software research includes themes of Algorithm and The Internet.
His Software engineering study incorporates themes from Java and World Wide Web.
His Test case research is multidisciplinary, incorporating perspectives in Reliability engineering, Regression testing and Fault detection and isolation.
Hong Mei has included themes like Software system, Hardware architecture and Component in his Software architecture study.
His Data mining research is multidisciplinary, incorporating elements of Consistency, Unified Modeling Language, Correctness and Constraint.
His most cited work include:
- Personalized QoS Prediction forWeb Services via Collaborative Filtering (335 citations)
- MAPO: Mining and Recommending API Usage Patterns (318 citations)
- Towards automatic model synchronization from model transformations (146 citations)
What are the main themes of his work throughout his whole career to date?
Hong Mei mainly focuses on Software engineering, Software, Distributed computing, Software architecture and Data mining.
The concepts of his Software engineering study are interwoven with issues in Software system, Component-based software engineering, Software development, Resource-oriented architecture and Middleware.
The study incorporates disciplines such as Java, Reliability engineering and Formal specification in addition to Software.
His biological study spans a wide range of topics, including Application server, Scalability, Key, Resource and Cloud computing.
His biological study spans a wide range of topics, including Systems engineering, Component and Runtime system.
Hong Mei has researched Data mining in several fields, including Feature model, Feature, Consistency, Test case and Constraint.
He most often published in these fields:
- Software engineering (26.04%)
- Software (21.60%)
- Distributed computing (18.05%)
What were the highlights of his more recent work (between 2013-2021)?
- Software (21.60%)
- Software engineering (26.04%)
- Operating system (7.10%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Software, Software engineering, Operating system, Cloud computing and Data mining.
His study on Software bug is often connected to Perspective as part of broader study in Software.
The various areas that he examines in his Software engineering study include Software system, Software development, Software construction and Software automation.
His Cloud computing study integrates concerns from other disciplines, such as Distributed computing, Response time, Cluster analysis, Service provider and Server.
Hong Mei combines subjects such as Quality of service, Scalability, Resource allocation and CloudSim with his study of Distributed computing.
His Data mining research focuses on Feature and how it connects with Pattern recognition.
Between 2013 and 2021, his most popular works were:
- Boosting Bug-Report-Oriented Fault Localization with Segmentation and Stack-Trace Analysis (101 citations)
- A Unified Test Case Prioritization Approach (62 citations)
- Fixing Recurring Crash Bugs via Analyzing Q&A Sites (T) (59 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Software
- Programming language
His primary scientific interests are in Software, Software bug, Software engineering, Empirical research and Code.
As a part of the same scientific study, Hong Mei usually deals with the Software, concentrating on Source code and frequently concerns with Crash, Bebugging and Software system.
His Software bug research includes themes of Debugging, Embedded system and Resource management.
As part of the same scientific family, Hong Mei usually focuses on Software engineering, concentrating on Software artifacts and intersecting with World Wide Web.
His World Wide Web course of study focuses on Component and Mashup, Web service and Service.
In his study, which falls under the umbrella issue of Data mining, Regression testing is strongly linked to Test.
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