D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 35 Citations 4,518 107 World Ranking 6085 National Ranking 155

Overview

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.

Best Publications

When and why your code starts to smell bad

Michele Tufano;Fabio Palomba;Gabriele Bavota;Rocco Oliveto.
international conference on software engineering (2015)

259 Citations

Detecting bad smells in source code using change history information

Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Rocco Oliveto.
automated software engineering (2013)

227 Citations

Do They Really Smell Bad? A Study on Developers' Perception of Bad Code Smells

Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Rocco Oliveto.
international conference on software maintenance (2014)

218 Citations

Mining Version Histories for Detecting Code Smells

Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Rocco Oliveto.
IEEE Transactions on Software Engineering (2015)

214 Citations

An experimental investigation on the innate relationship between quality and refactoring

Gabriele Bavota;Andrea De Lucia;Massimiliano Di Penta;Rocco Oliveto.
Journal of Systems and Software (2015)

180 Citations

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Fausto Fasano.
Empirical Software Engineering (2018)

158 Citations

User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps

Fabio Palomba;Mario Linares-Vasquez;Gabriele Bavota;Rocco Oliveto.
international conference on software maintenance (2015)

154 Citations

When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)

Michele Tufano;Fabio Palomba;Gabriele Bavota;Rocco Oliveto.
IEEE Transactions on Software Engineering (2017)

121 Citations

A textual-based technique for Smell Detection

Fabio Palomba;Annibale Panichella;Andrea De Lucia;Rocco Oliveto.
international conference on program comprehension (2016)

106 Citations

Recommending and localizing change requests for mobile apps based on user reviews

Fabio Palomba;Pasquale Salza;Adelina Ciurumelea;Sebastiano Panichella.
international conference on software engineering (2017)

105 Citations

Best Scientists Citing Fabio Palomba

Alessandro Garcia

Alessandro Garcia

Pontifical Catholic University of Rio de Janeiro

Publications: 38

Marouane Kessentini

Marouane Kessentini

University of Michigan–Ann Arbor

Publications: 34

Gabriele Bavota

Gabriele Bavota

Universita della Svizzera Italiana

Publications: 28

Foutse Khomh

Foutse Khomh

Polytechnique Montréal

Publications: 27

Denys Poshyvanyk

Denys Poshyvanyk

William & Mary

Publications: 23

Yann-Gaël Guéhéneuc

Yann-Gaël Guéhéneuc

Concordia University

Publications: 19

Xin Xia

Xin Xia

Monash University

Publications: 18

David Lo

David Lo

Singapore Management University

Publications: 17

Alexander Serebrenik

Alexander Serebrenik

Eindhoven University of Technology

Publications: 16

Paris Avgeriou

Paris Avgeriou

University of Groningen

Publications: 16

Mario Linares-Vasquez

Mario Linares-Vasquez

Universidad de Los Andes

Publications: 15

Massimiliano Di Penta

Massimiliano Di Penta

University of Sannio

Publications: 15

Rocco Oliveto

Rocco Oliveto

University of Molise

Publications: 14

Giuliano Antoniol

Giuliano Antoniol

Polytechnique Montréal

Publications: 13

Harald C. Gall

Harald C. Gall

University of Zurich

Publications: 12

Kenichi Matsumoto

Kenichi Matsumoto

Nara Institute of Science and Technology

Publications: 11

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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