What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Artificial intelligence
His main research concerns Software maintenance, Data mining, Source code, Programming language and Software engineering.
The study incorporates disciplines such as Program comprehension, Software design pattern, Software quality and Software evolution in addition to Software maintenance.
His study in Data mining is interdisciplinary in nature, drawing from both Machine learning and Probabilistic logic, Artificial intelligence.
His study on Object-oriented programming and Structural pattern is often connected to Copying as part of broader study in Programming language.
His biological study spans a wide range of topics, including Software, Software development and Empirical research.
His studies in Software integrate themes in fields like Java and Formal specification.
His most cited work include:
- DECOR: A Method for the Specification and Detection of Code and Design Smells (497 citations)
- Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (378 citations)
- Is it a bug or an enhancement?: a text-based approach to classify change requests (275 citations)
What are the main themes of his work throughout his whole career to date?
Yann-Gaël Guéhéneuc mostly deals with Software engineering, Software, Source code, Software maintenance and Software system.
The Software engineering study combines topics in areas such as Quality, Software quality, Software development, Code smell and Software design pattern.
His Code smell research is multidisciplinary, incorporating perspectives in Context and Spaghetti code.
The various areas that Yann-Gaël Guéhéneuc examines in his Source code study include TRACE, Program comprehension, Data mining and Information retrieval.
His Data mining research includes elements of Precision and recall, Machine learning, Artificial intelligence and Traceability.
His research ties Java and Software maintenance together.
He most often published in these fields:
- Software engineering (37.74%)
- Software (22.64%)
- Source code (18.49%)
What were the highlights of his more recent work (between 2019-2021)?
- Software engineering (37.74%)
- Software (22.64%)
- Context (10.57%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Software engineering, Software, Context, Video game and Quality.
His work deals with themes such as Identification, Microservices, Code refactoring and Software quality, Code smell, which intersect with Software engineering.
In his research on the topic of Software quality, Software system is strongly related with Design pattern.
As a part of the same scientific family, he mostly works in the field of Software, focusing on Scalability and, on occasion, Communications protocol, Interoperability, Debugging and Abstraction.
In his work, Pair programming, Spaghetti code, Information retrieval and Task is strongly intertwined with Program comprehension, which is a subfield of Context.
His work carried out in the field of Quality brings together such families of science as Delimiter and Use case.
Between 2019 and 2021, his most popular works were:
- Code smells and refactoring: A tertiary systematic review of challenges and observations (15 citations)
- A systematic literature review on automated log abstraction techniques (11 citations)
- A practical guide on conducting eye tracking studies in software engineering (9 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Software
- Operating system
His scientific interests lie mostly in Context, Software engineering, Video game, Software and Program comprehension.
His Context research is multidisciplinary, incorporating elements of Root, Card sorting, Stack overflow and Engineering management.
His Software engineering research integrates issues from Quality and Code refactoring.
He works mostly in the field of Quality, limiting it down to concerns involving Code smell and, occasionally, Maintainability.
His study in the field of Pair programming also crosses realms of BitTorrent tracker.
His research in Program comprehension intersects with topics in Machine learning, False positive paradox, Feature, God object and Study software.
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