What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Software engineering
His primary areas of study are Software engineering, Programming language, Code smell, Java and Code refactoring.
His Software engineering research integrates issues from Enterprise architecture framework, Applications architecture, View model, Space-based architecture and Software architecture description.
His work investigates the relationship between Programming language and topics such as Variety that intersect with problems in Component, Formal specification, Open architecture and COBOL.
In his study, Data science is inextricably linked to Software maintenance, which falls within the broad field of Code smell.
His work deals with themes such as Aspect-oriented programming, Maintainability, Data mining and Reverse engineering, which intersect with Java.
His study in Code refactoring is interdisciplinary in nature, drawing from both Unit testing and Extreme programming.
His most cited work include:
- A Systematic Survey of Program Comprehension through Dynamic Analysis (314 citations)
- Java quality assurance by detecting code smells (307 citations)
- The ASF+SDF Meta-environment: A Component-Based Language Development Environment (306 citations)
What are the main themes of his work throughout his whole career to date?
Leon Moonen mainly focuses on Software engineering, Software system, Programming language, Software and Data mining.
His Software engineering research is multidisciplinary, incorporating elements of Code refactoring, Software maintenance, Software development, Reverse engineering and Source code.
He interconnects Program comprehension, Maintainability and Code smell in the investigation of issues within Software maintenance.
His research investigates the connection with Programming language and areas like Natural language processing which intersect with concerns in Subtyping.
His work in the fields of Software, such as MISRA C and Extreme programming, overlaps with other areas such as Quality.
His Data mining research includes elements of Change impact analysis, Data science and Software inspection.
He most often published in these fields:
- Software engineering (47.15%)
- Software system (30.89%)
- Programming language (26.83%)
What were the highlights of his more recent work (between 2015-2021)?
- Data mining (21.14%)
- Software system (30.89%)
- Association rule learning (6.50%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data mining, Software system, Association rule learning, Change impact analysis and Software.
His work carried out in the field of Data mining brings together such families of science as Principal component analysis and Dimensionality reduction.
His biological study focuses on Program comprehension.
His Change impact analysis study incorporates themes from Software maintenance, Data science and Artificial intelligence.
His Software engineering research extends to Software, which is thematically connected.
Leon Moonen has included themes like Anomaly detection, Code review, Artificial immune system and Robustness in his Software engineering study.
Between 2015 and 2021, his most popular works were:
- An Industrial Survey of Safety Evidence Change Impact Analysis Practice (42 citations)
- Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis (29 citations)
- Exploring the Effects of History Length and Age on Mining Software Change Impact (12 citations)
In his most recent research, the most cited papers focused on:
- Software
- Programming language
- Software engineering
Association rule learning, Data mining, Change impact analysis, Software system and Algorithm design are his primary areas of study.
His Association rule learning research is multidisciplinary, relying on both Value, Variety and Contrast.
Change impact analysis is a subfield of Software that Leon Moonen explores.
His Software study combines topics in areas such as System safety, Systems engineering and Safety standards.
Specifically, his work in Software system is concerned with the study of Software maintenance.
His studies examine the connections between Algorithm design and genetics, as well as such issues in Measure, with regards to Machine learning, Artificial intelligence, Data set, Set and Unsupervised learning.
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