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.
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.
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.
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.
Is it a bug or an enhancement?: a text-based approach to classify change requests
Giuliano Antoniol;Kamel Ayari;Massimiliano Di Penta;Foutse Khomh.
conference of the centre for advanced studies on collaborative research (2008)
An exploratory study of the impact of antipatterns on class change- and fault-proneness
Foutse Khomh;Massimiliano Di Penta;Yann-Gaël Guéhéneuc;Giuliano Antoniol.
Empirical Software Engineering (2012)
An Exploratory Study of the Impact of Code Smells on Software Change-proneness
Foutse Khomh;Massimiliano Di Penta;Yann-Gael Gueheneuc.
working conference on reverse engineering (2009)
An Empirical Study of the Impact of Two Antipatterns, Blob and Spaghetti Code, on Program Comprehension
Marwen Abbes;Foutse Khomh;Yann-Gael Gueheneuc;Giuliano Antoniol.
conference on software maintenance and reengineering (2011)
A Bayesian Approach for the Detection of Code and Design Smells
Foutse Khomh;Stéphane Vaucher;Yann-Gaël Guéhéneuc;Houari Sahraoui.
international conference on quality software (2009)
Do faster releases improve software quality?: an empirical case study of Mozilla Firefox
Foutse Khomh;Tejinder Dhaliwal;Ying Zou;Bram Adams.
mining software repositories (2012)
BDTEX: A GQM-based Bayesian approach for the detection of antipatterns
Foutse Khomh;Stephane Vaucher;Yann-Gaël Guéhéneuc;Houari Sahraoui.
Journal of Systems and Software (2011)
Do Design Patterns Impact Software Quality Positively
F. Khomh;Y.-G. Gueheneuc.
conference on software maintenance and reengineering (2008)
Do code review practices impact design quality? A case study of the Qt, VTK, and ITK projects
Rodrigo Morales;Shane McIntosh;Foutse Khomh.
ieee international conference on software analysis evolution and reengineering (2015)
On rapid releases and software testing: a case study and a semi-systematic literature review
Mika V. Mäntylä;Bram Adams;Foutse Khomh;Emelie Engström.
Empirical Software Engineering (2015)
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