World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
8424
World Ranking
8737
National Ranking
117

Kenichi Matsumoto 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 Kenichi Matsumoto 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: 369 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Kenichi Matsumoto 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 Kenichi Matsumoto 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Kenichi Matsumoto is affiliated with the Nara Institute of Science and Technology in Japan. Their research primarily focuses on the field of Computer Science, with a substantial body of work related to Software Engineering and Information Systems.

The scientist's publication record includes numerous papers concentrated in several subfields of study such as Information Systems, Computer Science Applications, Computer Networks and Communications, Software, and Information Systems and Management.

Key topics covered in their research encompass:

  • Software Engineering Research
  • Open Source Software Innovations
  • Software Engineering Techniques and Practices
  • Scientific Computing and Data Management
  • Software System Performance and Reliability
  • Software Reliability and Analysis Research
  • Artificial Intelligence in Healthcare and Education

Kenichi Matsumoto's research articles have been published in a variety of venues, with frequent contributions to:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEICE Transactions on Information and Systems
  • Empirical Software Engineering
  • IEEE Transactions on Software Engineering

Some of the recent papers include:

  • Predicting Defective Lines Using a Model-Agnostic Technique, 2020, IEEE Transactions on Software Engineering
  • SARS-CoV-2 infection initiates interleukin-17-enriched transcriptional response in different cells from multiple organs, 2021, Scientific Reports
  • Wait for it: identifying "On-Hold" self-admitted technical debt, 2020, Empirical Software Engineering
  • Meta-analysis of single-cell RNA-seq data reveals phenotypic switching of immune cells in severe COVID-19 patients, 2021, Computers in Biology and Medicine
  • Understanding shared links and their intentions to meet information needs in modern code review:, 2021, Empirical Software Engineering

Collaboration forms an important part of their research output, with frequent coauthors including:

  • Raula Gaikovina Kula
  • Hideaki Hata
  • Christoph Treude
  • Takashi Ishio
  • Tao Xiao

Best Publications

  • An Empirical Comparison of Model Validation Techniques for Defect Prediction Models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • The Impact of Automated Parameter Optimization on Defect Prediction Models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • Automated parameter optimization of classification techniques for defect prediction models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models

    Chakkrit Tantithamthavorn;Ahmed E. Hassan;Kenichi Matsumoto

  • Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review

    Patanamon Thongtanunam;Chakkrit Tantithamthavorn;Raula Gaikovina Kula;Norihiro Yoshida

  • Revisiting common bug prediction findings using effort-aware models

    Yasutaka Kamei;Shinsuke Matsumoto;Akito Monden;Ken-ichi Matsumoto

  • Software quality analysis by code clones in industrial legacy software

    A. Monden;D. Nakae;T. Kamiya;S. Sato;S. Sato

  • Analyzing individual performance of source code review using reviewers' eye movement

    Hidetake Uwano;Masahide Nakamura;Akito Monden;Ken-ichi Matsumoto

  • A practical method for watermarking Java programs

    A. Monden;H. Iida;K. Matsumoto;K. Inoue

  • Studying re-opened bugs in open source software

    Emad Shihab;Akinori Ihara;Yasutaka Kamei;Walid M. Ibrahim

  • The Effects of Over and Under Sampling on Fault-prone Module Detection

    Y. Kamei;A. Monden;S. Matsumoto;T. Kakimoto

  • Dynamic Software Birthmarks to Detect the Theft of Windows Applications

    Haruaki Tamada;Keiji Okamoto;Masahide Nakamura;Akito Monden

  • Predicting Re-opened Bugs: A Case Study on the Eclipse Project

    Emad Shihab;Akinori Ihara;Yasutaka Kamei;Walid M. Ibrahim

  • An analysis of developer metrics for fault prediction

    Shinsuke Matsumoto;Yasutaka Kamei;Akito Monden;Ken-ichi Matsumoto

  • Java Birthmarks —Detecting the Software Theft—

    Haruaki Tamada;Masahide Nakamura;Akito Monden;Ken-Ichi Matsumoto

  • The impact of mislabelling on the performance and interpretation of defect prediction models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Akinori Ihara

  • Predicting Defective Lines Using a Model-Agnostic Technique

    Supatsara Wattanakriengkrai;Patanamon Thongtanunam;Chakkrit Tantithamthavorn;Hideaki Hata

  • Constructing Home Network Systems and Integrated Services Using Legacy Home Appliances and Web Services

    Masahide Nakamura;Akihiro Tanaka;Hiroshi Igaki;Haruaki Tamada

  • Ginger2: an environment for computer-aided empirical software engineering

    K. Torii;K. Matsumoto;K. Nakakoji;Y. Takada

  • Exploiting self-modification mechanism for program protection

    Y. Kanzaki;A. Monden;M. Nakamura;K. Matsumoto

  • Accelerating cross-project knowledge collaboration using collaborative filtering and social networks

    Masao Ohira;Naoki Ohsugi;Tetsuya Ohoka;Ken-ichi Matsumoto

Frequent Co-Authors

Yasutaka Kamei
Yasutaka Kamei Kyushu University
Katsuro Inoue
Katsuro Inoue Ritsumeikan University
Chakkrit Tantithamthavorn
Chakkrit Tantithamthavorn Monash University
Ahmed E. Hassan
Ahmed E. Hassan Queen's University
Shane McIntosh
Shane McIntosh McGill University
Jacky Keung
Jacky Keung City University of Hong Kong
Christoph Treude
Christoph Treude Singapore Management University
Bram Adams
Bram Adams Queen's University
Meiyappan Nagappan
Meiyappan Nagappan University of Waterloo
Takeshi Itoh
Takeshi Itoh Nara Institute of Science and Technology

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