What is he best known for?
The fields of study he is best known for:
- Software
- Operating system
- Programming language
His primary areas of investigation include Code smell, Software, Source code, Code refactoring and Software system.
In his works, Fabio Palomba performs multidisciplinary study on Code smell and Empirical research.
He has researched Software in several fields, including World Wide Web and Data mining.
The concepts of his Software system study are interwoven with issues in Reliability engineering, Software engineering and Test case.
In his research on the topic of Code, Maintainability and Natural language processing is strongly related with Open source.
Fabio Palomba works mostly in the field of Code, limiting it down to topics relating to Computer security and, in certain cases, Software quality and Data science, as a part of the same area of interest.
His most cited work include:
- Mining Version Histories for Detecting Code Smells (153 citations)
- When and why your code starts to smell bad (145 citations)
- Detecting bad smells in source code using change history information (144 citations)
What are the main themes of his work throughout his whole career to date?
Software, Source code, Code smell, Empirical research and Software system are his primary areas of study.
His Software study incorporates themes from Relation, Information retrieval and Data science.
His Source code study also includes fields such as
- Artificial intelligence that connect with fields like Context and Data mining,
- Quality that intertwine with fields like Java.
Fabio Palomba works mostly in the field of Code smell, limiting it down to concerns involving Machine learning and, occasionally, Classifier.
His Software system research is multidisciplinary, incorporating elements of Reliability engineering, Software engineering and Code refactoring.
His work deals with themes such as Unit testing and Test case, which intersect with Software quality.
He most often published in these fields:
- Software (34.85%)
- Source code (31.82%)
- Code smell (30.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Source code (31.82%)
- Software engineering (25.00%)
- Artificial intelligence (20.45%)
In recent papers he was focusing on the following fields of study:
Fabio Palomba spends much of his time researching Source code, Software engineering, Artificial intelligence, Software and Code smell.
The study incorporates disciplines such as Static program analysis, Sociotechnical system, Code, Static analysis and Scripting language in addition to Source code.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Context, Maintainability, Proof of concept, Pipeline and Machine learning.
His Software study focuses on Software quality in particular.
His biological study spans a wide range of topics, including Software system and Code coverage.
Fabio Palomba has included themes like Identification and Software evolution in his Code smell study.
Between 2019 and 2021, his most popular works were:
- Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells? (26 citations)
- UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception (17 citations)
- How developers engage with static analysis tools in different contexts (16 citations)
In his most recent research, the most cited papers focused on:
- Software
- Operating system
- Programming language
Fabio Palomba mainly focuses on Software, Empirical research, Source code, Software development and Code smell.
His studies deal with areas such as Quality, Relation, Test case and Exploit as well as Software.
His research integrates issues of Software quality, Mutation, Lightweight methodology, Unit testing and Software engineering in his study of Source code.
While the research belongs to areas of Unit testing, Fabio Palomba spends his time largely on the problem of Code refactoring, intersecting his research to questions surrounding Android.
Fabio Palomba interconnects Orthogonality, Software evolution, Set, Artificial intelligence and Machine learning in the investigation of issues within Code smell.
His Machine learning research includes elements of Software system and Maintainability.
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.
