World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
14681
World Ranking
3078
National Ranking
183

Martin Shepperd 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 Martin Shepperd 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: 177 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Martin Shepperd 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 Martin Shepperd 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: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Overview

Martin Shepperd is affiliated with Brunel University London in the United Kingdom. Their research primarily spans the field of Computer Science, with a concentration on Information Systems, Software, Artificial Intelligence, Computer Networks and Communications, and Safety Research.

The scientist's research topics include:

  • Software Engineering Research
  • Software Reliability and Analysis Research
  • Imbalanced Data Classification Techniques
  • Software System Performance and Reliability
  • Academic integrity and plagiarism
  • Artificial Intelligence in Healthcare and Education
  • Software Engineering Techniques and Practices

Martin Shepperd has contributed to several publication venues, notably:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Information and Software Technology
  • PLoS ONE
  • Infant Behavior and Development

Among their recent papers are:

  • "A systematic review of unsupervised learning techniques for software defect prediction", 2020, Information and Software Technology
  • "The impact of using biased performance metrics on software defect prediction research", 2021, Information and Software Technology
  • "Changing the logic of replication: A case from infant studies", 2020, Infant Behavior and Development
  • "An analysis of retracted papers in Computer Science", 2023, PLoS ONE
  • "The impact of using biased performance metrics on software defect prediction research", 2021, arXiv (Cornell University)

Frequent coauthors collaborating with Martin Shepperd include:

  • Jingxiu Yao
  • Ning Li
  • Yuchen Guo
  • Fernando Brito e Abreu
  • Ricardo Pérez-Castillo

In addition to journal articles, Martin Shepperd has published a book titled Quality of Information and Communications Technology in 2020 with Springer Science+Business Media.

Best Publications

  • Estimating software project effort using analogies

    M. Shepperd;C. Schofield

  • A Systematic Review of Software Development Cost Estimation Studies

    M. Jorgensen;M. Shepperd

  • Data Quality: Some Comments on the NASA Software Defect Datasets

    M. Shepperd;Qinbao Song;Zhongbin Sun;C. Mair

  • What accuracy statistics really measure

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

  • A General Software Defect-Proneness Prediction Framework

    Qinbao Song;Zihan Jia;M Shepperd;Shi Ying

  • Evaluating prediction systems in software project estimation

    Martin Shepperd;Steve MacDonell

  • Researcher Bias: The Use of Machine Learning in Software Defect Prediction

    Martin Shepperd;David Bowes;Tracy Hall

  • Reformulating software engineering as a search problem

    J Clarke;J J Dolado;Mark Harman;R Hierons

  • Effort estimation using analogy

    Martin Shepperd;Chris Schofield;Barbara Kitchenham

  • Comparing software prediction techniques using simulation

    M. Shepperd;G. Kadoda

  • An empirical investigation of an object-oriented software system

    M. Cartwright;M. Shepperd

  • Reliability and validity in comparative studies of software prediction models

    I. Myrtveit;E. Stensrud;M. Shepperd

  • A critique of cyclomatic complexity as a software metric

    Martin J. Shepperd

  • An investigation of machine learning based prediction systems

    Carolyn Mair;Gada Kadoda;Martin Lefley;Keith Phalp

  • A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction

    Qinbao Song;Yuchen Guo;Martin Shepperd

  • Software defect association mining and defect correction effort prediction

    Qinbao Song;M. Shepperd;M. Cartwright;C. Mair

  • Comments on "A metrics suite for object oriented design

    N.I. Churcher;M.J. Shepperd;S. Chidamber;C.F. Kemerer

  • A systematic review of unsupervised learning techniques for software defect prediction

    Ning Li;Martin J. Shepperd;Yuchen Guo

  • Search Heuristics, Case-based Reasoning And Software Project Effort Prediction

    Colin Kirsopp;Martin J. Shepperd;John Hart

  • Practical software metrics for project management and process improvement: R Grady Prentice-Hall (1992) £30.95 282 pp ISBN 0 13 720384 5

    Martin J. Shepperd

  • Formulating software engineering as a search problem.

    John A. Clark;José Javier Dolado;Mark Harman;Robert M. Hierons

  • What accuracy statistics really measure

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

Frequent Co-Authors

Stephen G. MacDonell
Stephen G. MacDonell Victoria University of Wellington
Steve Counsell
Steve Counsell Brunel University London
Barbara Kitchenham
Barbara Kitchenham Keele University
Mark Harman
Mark Harman University College London
Magne Jørgensen
Magne Jørgensen Simula Research Laboratory
Tracy Hall
Tracy Hall Lancaster University
Marc Roper
Marc Roper University of Strathclyde
Tim Menzies
Tim Menzies North Carolina State University
Robert M. Hierons
Robert M. Hierons University of Sheffield
Per Runeson
Per Runeson Lund University

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