World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
6194
World Ranking
10227
National Ranking
27

Stephen G. MacDonell 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 Stephen G. MacDonell 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: 220 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Stephen G. MacDonell 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 Stephen G. MacDonell 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: 38 D-Index — 30th percentile

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

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

Overview

Stephen G. MacDonell is affiliated with Auckland University of Technology in New Zealand. Their research predominantly falls within the field of Computer Science, with a focus on several subfields including Information Systems, Artificial Intelligence, Computer Science Applications, Computer Networks and Communications, and Software.

The primary topics addressed in their work encompass Software Engineering Research, Software Engineering Techniques and Practices, Open Source Software Innovations, Advanced Malware Detection Techniques, Software Reliability and Analysis Research, Advanced Software Engineering Methodologies, and Non-Destructive Testing Techniques.

Their recent publications reflect a concentration on software development methodologies, fault diagnostics, and data-driven prediction models. Notable papers include:

  • What Makes Agile Software Development Agile?, 2021, IEEE Transactions on Software Engineering
  • Finding faults: A scoping study of fault diagnostics for Industrial Cyber-Physical Systems, 2020, Journal of Systems and Software
  • An empirical study on the effectiveness of data resampling approaches for cross-project software defect prediction, 2021, IET Software
  • Soil texture prediction with automated deep convolutional neural networks and population-based learning, 2023, Geoderma
  • Towards the statistical construction of hybrid development methods, 2020, Journal of Software Evolution and Process

Stephen G. MacDonell frequently collaborates with several co-authors. Those with the most joint publications include Sherlock A. Licorish, Ifeanyi G. Ndukwe, Amjed Tahir, Roopak Sinha, and Krassie Petrova.

Their works are regularly published in venues such as arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Journal of Systems and Software, and IEEE Transactions on Software Engineering.

Best Publications

  • What accuracy statistics really measure

    Barbara A. Kitchenham;Lesley Pickard;Stephen G. MacDonell;Martin J. Shepperd

  • Evaluating prediction systems in software project estimation

    Martin Shepperd;Steve MacDonell

  • Factors that affect software systems development project outcomes: A survey of research

    Laurie McLeod;Stephen G. MacDonell

  • A Perspective-Based Understanding of Project Success:

    Laurie McLeod;Bill Doolin;Stephen G. MacDonell

  • A comparison of techniques for developing predictive models of software metrics

    Andrew R. Gray;Stephen G. MacDonell

  • Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment

    A.R. Gray;S.G. MacDonell;M.J. Shepperd

  • How Reliable Are Systematic Reviews in Empirical Software Engineering

    S MacDonell;M Shepperd;B Kitchenham;E Mendes

  • Software Forensics: Extending Authorship Analysis Techniques to Computer Programs

    Stephen G MacDonell;Donna Buckingham;Andrew R Gray;Philip J Sallis

  • Combining techniques to optimize effort predictions in software project management

    Stephen G. MacDonell;Martin J. Shepperd

  • A Baseline Model for Software Effort Estimation

    Peter A. Whigham;Caitlin A. Owen;Stephen G. Macdonell

  • Applications of fuzzy logic to software metric models for development effort estimation

    A. Gray;S. MacDonell

  • Source Code Authorship Analysis For Supporting the Cybercrime Investigation Process.

    Georgia Frantzeskou;Stephen G. MacDonell;Efstathios Stamatatos

  • Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews

    Katia R. Felizardo;Norsaremah Salleh;Rafael M. Martins;Emilia Mendes

  • Understanding the attitudes, knowledge sharing behaviors and task performance of core developers: A longitudinal study

    Sherlock A. Licorish;Stephen G. MacDonell

  • Software Metrics Data Analysis—Exploring the RelativePerformance of Some Commonly Used Modeling Techniques

    Andrew R. Gray;Stephen G. Macdonell

  • A systematic mapping study on dynamic metrics and software quality

    Amjed Tahir;Stephen G. MacDonell

  • A comparison of modeling techniques for software development effort prediction

    Stephen G. MacDonell;Andrew R. Gray

  • What Makes Agile Software Development Agile

    Marco Kuhrmann;Paolo Tell;Regina Hebig;Jil Ann Christin Klunder

  • Examining the significance of high-level programming features in source code author classification

    Georgia Frantzeskou;Stephen MacDonell;Efstathios Stamatatos;Stefanos Gritzalis

  • Technical debt and agile software development practices and processes: An industry practitioner survey

    Johannes Holvitie;Johannes Holvitie;Sherlock A. Licorish;Rodrigo O. Spínola;Sami Hyrynsalmi

  • Source code authorship analysis for supporting the cybercrime investigation process

    Georgia Frantzeskou;Stefanos Gritzalis;Stephen G. MacDonell

  • What accuracy statistics really measure

    Barbara Ann Kitchenham;Stephen G. MacDonell;Lesley M. Pickard;Martin J. Shepperd

Frequent Co-Authors

Martin Shepperd
Martin Shepperd Brunel University London
José Carlos Maldonado
José Carlos Maldonado Universidade de São Paulo
Nikola Kasabov
Nikola Kasabov Auckland University of Technology
Emilia Mendes
Emilia Mendes Aarhus University
Jürgen Münch
Jürgen Münch Reutlingen University
Jacky Keung
Jacky Keung City University of Hong Kong
Stefanos Gritzalis
Stefanos Gritzalis University of Piraeus
Barbara Kitchenham
Barbara Kitchenham Keele University
Efstathios Stamatatos
Efstathios Stamatatos University of the Aegean
Steve Counsell
Steve Counsell Brunel University London

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