World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
7488
World Ranking
8009
National Ranking
72

Paul Grünbacher 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 Paul Grünbacher 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: 246 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Paul Grünbacher 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 Paul Grünbacher 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

Paul Grünbacher is affiliated with Johannes Kepler University of Linz in Austria and has contributed extensively in the field of computer science, with an emphasis on advanced software engineering methodologies. Their research spans multiple subfields including artificial intelligence, information systems, software, computer networks and communications, as well as industrial and manufacturing engineering.

They have published 33 works related to computer science, with notable focus areas such as:

  • Advanced Software Engineering Methodologies
  • Software Engineering Research
  • Software System Performance and Reliability
  • Model-Driven Software Engineering Techniques
  • Service-Oriented Architecture and Web Services
  • Flexible and Reconfigurable Manufacturing Systems
  • Product Development and Customization

Key recent publications by Paul Grünbacher include:

  • Concepts of variation control systems, 2020, Journal of Systems and Software
  • Evolution in dynamic software product lines, 2020, Journal of Software Evolution and Process
  • Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study, 2020, Proceedings of the ACM on Human-Computer Interaction
  • Supporting feature-oriented evolution in industrial automation product lines, 2020, Concurrent Engineering
  • Guiding feature model evolution by lifting code-level dependencies, 2021, Journal of Computer Languages

The scientist frequently collaborates with other researchers in the software engineering domain, including:

  • Lukas Linsbauer
  • Alexander Egyed
  • Gabriela Karoline Michelon
  • David Obermann
  • Wesley K. G. Assunção

Paul Grünbacher's research is regularly published in established venues such as:

  • Empirical Software Engineering
  • OPAL (Open@LaTrobe) (La Trobe University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Systems and Software
  • Journal of Software Evolution and Process

Best Publications

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

    Krzysztof Czarnecki;Paul Grünbacher;Rick Rabiser;Klaus Schmid

  • Developing groupware for requirements negotiation: lessons learned

    B. Boehm;P. Grunbacher;R.O. Briggs

  • The DOPLER meta-tool for decision-oriented variability modeling: a multiple case study

    Deepak Dhungana;Paul Grünbacher;Rick Rabiser

  • Automating requirements traceability: Beyond the record & replay paradigm

    A. Egyed;P. Grunbacher

  • What is a feature?: a qualitative study of features in industrial software product lines

    Thorsten Berger;Daniela Lettner;Julia Rubin;Paul Grünbacher

  • A comparison of decision modeling approaches in product lines

    Klaus Schmid;Rick Rabiser;Paul Grünbacher

  • Identifying requirements conflicts and cooperation: how quality attributes and automated traceability can help

    A. Egyed;P. Grunbacher

  • Reconciling software requirements and architectures with intermediate models

    Paul Grünbacher;Alexander Egyed;Nenad Medvidovic

  • A systematic review and an expert survey on capabilities supporting multi product lines

    Gerald Holl;Paul Grünbacher;Rick Rabiser

  • Reconciling software requirements and architectures: the CBSP approach

    P. Grunbacher;A. Egyed;N. Medvidovic

  • Value-Based Requirements Traceability: Lessons Learned

    Alexander Egyed;Paul Grunbacher;Matthias Heindl;Stefan Biffl

  • EasyWinWin: managing complexity in requirements negotiation with GSS

    R.O. Briggs;P. Gruenbacher

  • Requirements for product derivation support: Results from a systematic literature review and an expert survey

    Rick Rabiser;Paul Grünbacher;Deepak Dhungana

  • Traceability Fundamentals

    Unknown

  • Structuring the modeling space and supporting evolution in software product line engineering

    Deepak Dhungana;Paul Grünbacher;Rick Rabiser;Thomas Neumayer

  • Supporting Product Derivation by Adapting and Augmenting Variability Models

    R. Rabiser;P. Grunbacher;D. Dhungana

  • Surfacing tacit knowledge in requirements negotiation: experiences using EasyWinWin

    P. Grunbacher;R.O. Briggs

  • Agile product line planning: A collaborative approach and a case study

    Muhammad A. Noor;Rick Rabiser;Paul Grünbacher

  • The quest for Ubiquity: A roadmap for software and systems traceability research

    O. Gotel;J. Cleland-Huang;J. Huffman Hayes;A. Zisman

  • The Grand Challenge of Traceability (v1.0)

    Orlena Gotel;Jane Cleland-Huang;Jane Huffman Hayes;Andrea Zisman

  • Value-Based Management of Software Testing

    Rudolf Ramler;Stefan Biffl;Paul Grünbacher

Frequent Co-Authors

Rick Rabiser
Rick Rabiser Johannes Kepler University of Linz
Alexander Egyed
Alexander Egyed Johannes Kepler University of Linz
Neil Maiden
Neil Maiden City, University of London
Robert O. Briggs
Robert O. Briggs San Diego State University
Nenad Medvidovic
Nenad Medvidovic University of Southern California
Barry Boehm
Barry Boehm University of Southern California
David Benavides
David Benavides University of Seville
Jane Cleland-Huang
Jane Cleland-Huang University of Notre Dame
Luciano Baresi
Luciano Baresi Polytechnic University of Milan

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

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science opens doors to numerous in-demand career pathways. Many students also consider earning credentials in related fields. For example, if you are interested in engineering, you can look into electrical engineering degree online admissions to discover online programs that blend tech skills with advanced problem-solving abilities.

If you’re seeking a quick entry into the workforce, certifications for jobs are a practical way to boost your resume without a long-term commitment. Options range from network security to software development, offering flexible learning that can quickly lead to well-paying positions.

For graduates aiming to accelerate their education, the quickest cheapest masters degree routes make it possible to earn an advanced credential in less time and at an affordable cost. Such paths allow you to specialize while balancing work and study.

Finally, choosing one of the most useful graduate degrees can significantly enhance your job prospects. Degrees in computer science, data science, and engineering continue to rank among the most valuable programs for today’s technology-driven market.

Best Scientists Citing Paul Grünbacher

Trending Scientists

Recently Published Articles