What is he best known for?
The fields of study he is best known for:
- Software
- Operating system
- Programming language
Foutse Khomh spends much of his time researching Software quality, Software engineering, Context, Software and Code smell.
In his work, Software release life cycle and Release engineering is strongly intertwined with Quality, which is a subfield of Software quality.
Borrowing concepts from Empirical research, Foutse Khomh weaves in ideas under Software engineering.
Foutse Khomh interconnects Software maintenance, Class, Spaghetti code, Artificial intelligence and Machine learning in the investigation of issues within Code smell.
His research investigates the connection between Software maintenance and topics such as Software evolution that intersect with issues in Software design, Process management and Software design pattern.
His work on Bayesian network, Bayesian probability, Decision tree and Naive Bayes classifier is typically connected to Restructuring as part of general Artificial intelligence study, connecting several disciplines of science.
His most cited work include:
- Is it a bug or an enhancement?: a text-based approach to classify change requests (275 citations)
- An exploratory study of the impact of antipatterns on class change- and fault-proneness (242 citations)
- An Exploratory Study of the Impact of Code Smells on Software Change-proneness (204 citations)
What are the main themes of his work throughout his whole career to date?
Foutse Khomh focuses on Software engineering, Software, Empirical research, Software quality and Software system.
The various areas that Foutse Khomh examines in his Software engineering study include Quality, Context, Software maintenance, Task and Software design pattern.
His research integrates issues of Computer security, Documentation, Java and Artificial intelligence in his study of Software.
His study in Software quality focuses on Code smell in particular.
His Code smell research integrates issues from Machine learning and Functional requirement.
The study incorporates disciplines such as Anti-pattern, Deep learning and Code refactoring in addition to Software system.
He most often published in these fields:
- Software engineering (33.65%)
- Software (31.25%)
- Empirical research (20.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Software (31.25%)
- Software engineering (33.65%)
- Empirical research (20.67%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Software, Software engineering, Empirical research, Software quality and Code.
His studies deal with areas such as Java, Cyber-physical system, Documentation and Database as well as Software.
His Software engineering study integrates concerns from other disciplines, such as Software design, Software system, Software design pattern, Code refactoring and Code smell.
His Code smell study combines topics from a wide range of disciplines, such as Functional requirement and Commit.
His Software quality study frequently involves adjacent topics like Context.
The concepts of his Code study are interwoven with issues in Buffer overflow, Stack overflow, Type safety and Class.
Between 2019 and 2021, his most popular works were:
- On testing machine learning programs (22 citations)
- Automatic Mining of Opinions Expressed About APIs in Stack Overflow (11 citations)
- Understanding How and Why Developers Seek and Analyze API-Related Opinions (9 citations)
In his most recent research, the most cited papers focused on:
- Software
- Operating system
- Artificial intelligence
Foutse Khomh mostly deals with Empirical research, Software, Artificial intelligence, Machine learning and World Wide Web.
Software is closely attributed to Database in his study.
His Artificial intelligence study incorporates themes from Baseline and Code smell.
His Code smell research incorporates themes from Key, Software metric, Rank and Software evolution.
His World Wide Web research includes themes of Java, Commit and Code reuse.
His studies in Application programming interface integrate themes in fields like Context and Software quality.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
