H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 68 Citations 28,587 212 World Ranking 956 National Ranking 36

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Operating system

His primary areas of investigation include Feature model, Feature, Artificial intelligence, Data mining and Software engineering. His research in Feature model intersects with topics in ATLAS Transformation Language, Feature based, Model transformation language and Domain analysis. His work carried out in the field of Feature brings together such families of science as Cardinality, Theoretical computer science, Unified Modeling Language and Product.

His studies deal with areas such as Machine learning and Semantics as well as Artificial intelligence. His research investigates the connection between Data mining and topics such as Software that intersect with issues in Reverse engineering, Heuristic and Decision model. His work deals with themes such as Programming in the large and programming in the small, Symbolic programming, Software construction and Systems engineering, which intersect with Software engineering.

His most cited work include:

  • Generative Programming: Methods, Tools, and Applications (2205 citations)
  • Feature-based survey of model transformation approaches (856 citations)
  • Classification of Model Transformation Approaches (654 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Software engineering, Artificial intelligence, Software, Feature and Programming language. His Software engineering research is multidisciplinary, incorporating perspectives in Modeling language, Software development, Generative grammar and Systems engineering. He combines subjects such as Machine learning, Task and Pattern recognition with his study of Artificial intelligence.

The Software study combines topics in areas such as Data mining and Product. The various areas that Krzysztof Czarnecki examines in his Feature study include Cardinality, Theoretical computer science and Linux kernel. His work in the fields of Component overlaps with other areas such as Eclipse.

He most often published in these fields:

  • Software engineering (25.91%)
  • Artificial intelligence (22.26%)
  • Software (17.94%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (22.26%)
  • Pattern recognition (3.99%)
  • Object detection (3.65%)

In recent papers he was focusing on the following fields of study:

Krzysztof Czarnecki focuses on Artificial intelligence, Pattern recognition, Object detection, Machine learning and Feature. His Artificial intelligence research focuses on Task and how it relates to Reinforcement learning. As part of one scientific family, Krzysztof Czarnecki deals mainly with the area of Pattern recognition, narrowing it down to issues related to the Contextual image classification, and often Entropy and Kullback–Leibler divergence.

His work on Precision and recall is typically connected to Influence factor, Sampling and Rare events as part of general Machine learning study, connecting several disciplines of science. The concepts of his Feature study are interwoven with issues in Computer engineering, Linux kernel, Code refactoring, Systems design and Kernel. Krzysztof Czarnecki usually deals with Component and limits it to topics linked to Workflow and Software.

Between 2018 and 2021, his most popular works were:

  • FANTrack: 3D Multi-Object Tracking with Feature Association Network (31 citations)
  • A Study of Feature Scattering in the Linux Kernel (24 citations)
  • SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines (21 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Operating system

Krzysztof Czarnecki mostly deals with Artificial intelligence, Object detection, Detector, Computer vision and Pattern recognition. His Artificial intelligence research is multidisciplinary, relying on both Collision and Machine learning. His Machine learning study integrates concerns from other disciplines, such as Event and Bounded rationality.

His Pattern recognition study combines topics in areas such as Contextual image classification, Similarity and Inference. His Deep learning study incorporates themes from Domain, Multi-objective optimization, Task and Feature. Krzysztof Czarnecki integrates Feature and Scattering in his studies.

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.

Top Publications

Generative Programming

Barbara Barth;Gregory Butler;Krzysztof Czarnecki;Ulrich W. Eisenecker.
ECOOP '02 Proceedings of the Workshops and Posters on Object-Oriented Technology (2001)

5043 Citations

Generative Programming: Methods, Tools, and Applications

Krzysztof Czarnecki;Ulrich W. Eisenecker.
(2000)

3645 Citations

Model-Driven Software Development: Technology, Engineering, Management

Thomas Stahl;Markus Voelter;Krzysztof Czarnecki.
(2006)

1625 Citations

Feature-based survey of model transformation approaches

K. Czarnecki;S. Helsen.
Ibm Systems Journal (2006)

1525 Citations

Classification of Model Transformation Approaches

Krzysztof Czarnecki;Simon Helsen.
(2003)

1427 Citations

Formalizing cardinality‐based feature models and their specialization

Krzysztof Czarnecki;Simon Helsen;Ulrich W. Eisenecker.
Software Process: Improvement and Practice (2005)

861 Citations

Mapping features to models: a template approach based on superimposed variants

Krzysztof Czarnecki;Michał Antkiewicz.
generative programming and component engineering (2005)

773 Citations

Staged Configuration Using Feature Models

Krzysztof Czarnecki;Simon Helsen;Ulrich Eisenecker.
software product lines (2004)

719 Citations

Staged configuration through specialization and multilevel configuration of feature models

Krzysztof Czarnecki;Simon Helsen;Ulrich W. Eisenecker.
Software Process: Improvement and Practice (2005)

657 Citations

Feature Diagrams and Logics: There and Back Again

K. Czarnecki;A. Wasowski.
software product lines (2007)

445 Citations

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

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Krzysztof Czarnecki

Sven Apel

Sven Apel

Saarland University

Publications: 118

Gunter Saake

Gunter Saake

Otto-von-Guericke University Magdeburg

Publications: 99

Christian Kästner

Christian Kästner

Carnegie Mellon University

Publications: 98

Ina Schaefer

Ina Schaefer

Technische Universität Braunschweig

Publications: 77

Patrick Heymans

Patrick Heymans

University of Namur

Publications: 74

Zhenjiang Hu

Zhenjiang Hu

Peking University

Publications: 62

Don Batory

Don Batory

The University of Texas at Austin

Publications: 61

Manuel Wimmer

Manuel Wimmer

Johannes Kepler University of Linz

Publications: 59

Paul Grünbacher

Paul Grünbacher

Johannes Kepler University of Linz

Publications: 59

Alexander Egyed

Alexander Egyed

Johannes Kepler University of Linz

Publications: 57

Bernhard Rumpe

Bernhard Rumpe

RWTH Aachen University

Publications: 54

Andy Schürr

Andy Schürr

TU Darmstadt

Publications: 51

Jeff Gray

Jeff Gray

University of Alabama

Publications: 48

Carlos José Pereira de Lucena

Carlos José Pereira de Lucena

Pontifical Catholic University of Rio de Janeiro

Publications: 39

Juan de Lara

Juan de Lara

Autonomous University of Madrid

Publications: 37

Something went wrong. Please try again later.