World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8336
World Ranking
7952
National Ranking
35

Andrzej Wąsowski publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Andrzej Wąsowski sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 158 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Andrzej Wąsowski D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Andrzej Wąsowski sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Andrzej Wąsowski is affiliated with the IT University of Copenhagen in Denmark. Their research spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Information Systems, Software, Computational Theory and Mathematics, and Computer Science Applications.

Their main research topics encompass:

  • Formal Methods in Verification
  • Software Engineering Research
  • Logic, programming, and type systems
  • Software Testing and Debugging Techniques
  • Model-Driven Software Engineering Techniques
  • Privacy-Preserving Technologies in Data
  • Advanced Software Engineering Methodologies

Recent publications by Andrzej Wąsowski include:

  • "Behavior Trees and State Machines in Robotics Applications", 2023, IEEE Transactions on Software Engineering
  • "MROS: runtime adaptation for robot control architectures", 2022, Advanced Robotics
  • "Generalized abstraction-refinement for game-based CTL lifted model checking", 2020, Theoretical Computer Science
  • "Formal Specification and Testing for Reinforcement Learning", 2023, Proceedings of the ACM on Programming Languages
  • "Pull Request Governance in Open Source Communities", 2021, IEEE Transactions on Software Engineering

They frequently collaborate with several coauthors, including:

  • Einar Broch Johnsen
  • Raúl Pardo
  • Gijs van der Hoorn
  • Adam Alami
  • Darko Bozhinoski

Key publication venues for their work include:

  • arXiv (Cornell University)
  • Research Portal (King's College London)
  • IEEE Transactions on Software Engineering
  • Theoretical Computer Science
  • Empirical Software Engineering

Best Publications

  • A survey of variability modeling in industrial practice

    Thorsten Berger;Ralf Rublack;Divya Nair;Joanne M. Atlee

  • Feature Diagrams and Logics: There and Back Again

    K. Czarnecki;A. Wasowski

  • Cool features and tough decisions: a comparison of variability modeling approaches

    Krzysztof Czarnecki;Paul Grünbacher;Rick Rabiser;Klaus Schmid

  • Reverse engineering feature models

    Steven She;Rafael Lotufo;Thorsten Berger;Andrzej Wasowski

  • SAT-based analysis of feature models is easy

    Marcilio Mendonca;Andrzej Wąsowski;Krzysztof Czarnecki

  • Modal I/O automata for interface and product line theories

    Kim G. Larsen;Ulrik Nyman;Andrzej Wąsowski

  • Timed I/O automata: a complete specification theory for real-time systems

    Alexandre David;Kim G. Larsen;Axel Legay;Ulrik Nyman

  • Evolution of the linux kernel variability model

    Rafael Lotufo;Steven She;Thorsten Berger;Krzysztof Czarnecki

  • A Study of Variability Models and Languages in the Systems Software Domain

    Thorsten Berger;Steven She;Rafael Lotufo;Andrzej Wasowski

  • On modal refinement and consistency

    Kim G. Larsen;Ulrik Nyman;Andrzej Wasowski

  • Variability-aware performance prediction: a statistical learning approach

    Jianmei Guo;Krzysztof Czarnecki;Sven Apely;Norbert Siegmundy

  • Variability modeling in the real: a perspective from the operating systems domain

    Thorsten Berger;Steven She;Rafael Lotufo;Andrzej Wąsowski

  • Efficient compilation techniques for large scale feature models

    Marcilio Mendonca;Andrzej Wasowski;Krzysztof Czarnecki;Donald Cowan

  • Feature and meta-models in Clafer: mixed, specialized, and coupled

    Kacper Bak;Krzysztof Czarnecki;Andrzej Wasowski

  • 42 variability bugs in the linux kernel: a qualitative analysis

    Unknown

  • Sample Spaces and Feature Models: There and Back Again

    K. Czarnecki;S. She;A. Wasowski

  • Clafer: unifying class and feature modeling

    Kacper Bąk;Zinovy Diskin;Michał Antkiewicz;Krzysztof Czarnecki

  • CVL: common variability language

    Øystein Haugen;Andrzej Wąsowski;Krzysztof Czarnecki

  • Moving from specifications to contracts in component-based design

    Sebastian S. Bauer;Alexandre David;Rolf Hennicker;Kim Guldstrand Larsen

  • Data-efficient performance learning for configurable systems

    Jianmei Guo;Dingyu Yang;Norbert Siegmund;Sven Apel

  • Flexible product line engineering with a virtual platform

    Michał Antkiewicz;Wenbin Ji;Thorsten Berger;Krzysztof Czarnecki

  • Efficient synthesis of feature models

    Nele Andersen;Krzysztof Czarnecki;Steven She;Andrzej Wąsowski

Frequent Co-Authors

Krzysztof Czarnecki
Krzysztof Czarnecki University of Waterloo
Axel Legay
Axel Legay Université Catholique de Louvain
Kim Guldstrand Larsen
Kim Guldstrand Larsen Aalborg University
Thorsten Berger
Thorsten Berger Ruhr University Bochum
Alexandre David
Alexandre David Aalborg University
Richard F. Paige
Richard F. Paige McMaster University
Ina Schaefer
Ina Schaefer Technische Universität Braunschweig
Jean-Marc Jézéquel
Jean-Marc Jézéquel University of Rennes
Paulo Borba
Paulo Borba Federal University of Pernambuco
Ralf Lämmel
Ralf Lämmel Facebook (United States)

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