What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Software
Lu Zhang focuses on Artificial intelligence, Machine learning, Software, Test case and Artificial neural network.
Lu Zhang usually deals with Machine learning and limits it to topics linked to Test and Maintenance engineering, Correctness, Blocking and Integer programming.
His Software study focuses on Software bug in particular.
His Test case study combines topics in areas such as Reliability engineering, Process, Regression testing and Fault detection and isolation.
His work investigates the relationship between Reliability engineering and topics such as Test Management Approach that intersect with problems in Code coverage.
His biological study spans a wide range of topics, including Empirical research and Data mining.
His most cited work include:
- An approach to detecting duplicate bug reports using natural language and execution information (433 citations)
- MAPO: Mining and Recommending API Usage Patterns (318 citations)
- Searches for electroweak production of charginos, neutralinos, and sleptons decaying to leptons and W, Z, and Higgs bosons in pp collisions at 8 TeV (291 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Software, Data mining, Programming language and Test case.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Set and Natural language processing.
His studies in Software integrate themes in fields like Java, Software engineering and Source code.
As a part of the same scientific family, he mostly works in the field of Data mining, focusing on Software development and, on occasion, Software system and Information retrieval.
His work is dedicated to discovering how Programming language, Code are connected with Computer engineering and other disciplines.
Lu Zhang interconnects Reliability engineering, Debugging and Regression testing in the investigation of issues within Test case.
He most often published in these fields:
- Artificial intelligence (20.59%)
- Software (20.17%)
- Data mining (17.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (20.59%)
- Programming language (17.23%)
- Artificial neural network (7.98%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Programming language, Artificial neural network, Debugging and Code.
He combines subjects such as Machine learning, Natural language processing, Source code, Graph classification and Pattern recognition with his study of Artificial intelligence.
As a part of the same scientific study, he usually deals with the Programming language, concentrating on Commit and frequently concerns with Domain.
His Debugging study deals with Data mining intersecting with Range.
His work carried out in the field of Software bug brings together such families of science as Software system, Test case and Software testing.
The concepts of his Software study are interwoven with issues in Java, Construct and Software engineering.
Between 2018 and 2021, his most popular works were:
- An Empirical Study of Fault Localization Families and Their Combinations (50 citations)
- Predictive Mutation Testing (45 citations)
- Deep Learning Based Code Smell Detection (18 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Operating system
His scientific interests lie mostly in Debugging, Software, Artificial intelligence, Software system and Test.
His Debugging research incorporates elements of Range, Distributed computing, Data mining and Leverage.
His Software research includes elements of Correctness and Software engineering.
His study looks at the intersection of Artificial intelligence and topics like Machine learning with Software quality, Software testing and Test suite.
His Software system research incorporates elements of Supervised learning, Code coverage, Dimension and Scope.
His Test research also works with subjects such as
- Compiler that connect with fields like Test program and Scalability,
- Computer engineering which is related to area like Artificial neural network.
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