What is he best known for?
The fields of study he is best known for:
- Software
- Programming language
- Operating system
Tao Xie mainly investigates Software, Software engineering, Software development, Programming language and Software system.
His research integrates issues of Machine learning, World Wide Web and Natural language, Artificial intelligence in his study of Software.
His Software engineering study integrates concerns from other disciplines, such as Correctness, Access control, Consistency, Component-based software engineering and Software quality.
His Software development research incorporates themes from Class, Scalability, Unit testing and Automatic test pattern generation.
His study looks at the relationship between Programming language and fields such as Random testing, as well as how they intersect with chemical problems.
The various areas that Tao Xie examines in his Software system study include Bug tracking system, End user, Data science and Source code.
His most cited work include:
- An approach to detecting duplicate bug reports using natural language and execution information (433 citations)
- Parseweb: a programmer assistant for reusing open source code on the web (369 citations)
- MAPO: Mining and Recommending API Usage Patterns (318 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Software engineering, Software, Programming language, Software development and World Wide Web.
His Software engineering research is multidisciplinary, incorporating elements of Test strategy and Software performance testing, Software construction.
The Software study which covers Data mining that intersects with Code coverage.
His study in Source code, Unit testing, Object-oriented programming, Software system and Java is carried out as part of his studies in Programming language.
The concepts of his Source code study are interwoven with issues in Database and Code.
He works mostly in the field of Unit testing, limiting it down to topics relating to Symbolic execution and, in certain cases, Theoretical computer science.
He most often published in these fields:
- Software engineering (25.48%)
- Software (21.64%)
- Programming language (14.25%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (10.41%)
- Software engineering (25.48%)
- Software (21.64%)
In recent papers he was focusing on the following fields of study:
Tao Xie spends much of his time researching Artificial intelligence, Software engineering, Software, Software deployment and Machine learning.
Tao Xie combines subjects such as Java, Peer reviewing and Tracing with his study of Software engineering.
His Java study deals with Test prioritization intersecting with Code and Regression testing.
Debugging is closely connected to Data mining in his research, which is encompassed under the umbrella topic of Software.
His Software deployment study combines topics from a wide range of disciplines, such as Distributed computing, Delta Debugging, Construct, World Wide Web and Microservices.
Tao Xie has researched Natural language in several fields, including Semantics, Regular expression, Programming language and Test case.
Between 2017 and 2021, his most popular works were:
- An empirical study of Android test generation tools in industrial cases (32 citations)
- iDFlakies: A Framework for Detecting and Partially Classifying Flaky Tests (29 citations)
- Understanding Diverse Usage Patterns from Large-Scale Appstore-Service Profiles (29 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
His scientific interests lie mostly in Software engineering, Debugging, Microservices, Software and Artificial intelligence.
His Software engineering study combines topics in areas such as Test suite, User interface and Code coverage.
His study in Debugging is interdisciplinary in nature, drawing from both Software fault tolerance and Data mining.
Tao Xie works mostly in the field of Software fault tolerance, limiting it down to topics relating to Ranking and, in certain cases, Source code, as a part of the same area of interest.
His studies deal with areas such as Field and Dependability as well as Software.
His research links Machine learning with Artificial intelligence.
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