World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
6395
World Ranking
8085
National Ranking
1061

Jacky Keung 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 Jacky Keung 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: 165 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Jacky Keung 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 Jacky Keung 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

Jacky Keung is affiliated with City University of Hong Kong in China and focuses on research within the field of Computer Science. Their work primarily intersects with subfields such as Artificial Intelligence, Information Systems, Software, Computer Networks and Communications, and Signal Processing.

The scientist's main research topics include:

  • Software Engineering Research
  • Software Reliability and Analysis Research
  • Topic Modeling
  • Software Testing and Debugging Techniques
  • Software System Performance and Reliability
  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection

Keung has published extensively, with a strong presence in notable academic journals and platforms. Frequent publication venues include:

  • Information and Software Technology
  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • IEEE Transactions on Software Engineering
  • Journal of Systems and Software

Recent papers demonstrate a focus on software defect prediction, class imbalance issues, scheduling for cloud platforms, and software system optimization:

  • "Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction" (2021, Information and Software Technology)
  • "COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction" (2020, Information and Software Technology)
  • "A systematic review of scheduling approaches on multi-tenancy cloud platforms" (2020, Information and Software Technology)
  • "Predicting the precise number of software defects: Are we there yet?" (2022, Information and Software Technology)
  • "Improving the undersampling technique by optimizing the termination condition for software defect prediction" (2023, Expert Systems with Applications)

Their collaborative network features several frequent co-authors, including:

  • Xiao Yu
  • Zhen Yang
  • Xiaoxue Ma
  • Yan Xiao
  • Fengji Zhang

Best Publications

  • Empirical prediction models for adaptive resource provisioning in the cloud

    Sadeka Islam;Jacky Keung;Kevin Lee;Anna Liu

  • On the Value of Ensemble Effort Estimation

    E. Kocaguneli;T. Menzies;J. W. Keung

  • Robust Statistical Methods for Empirical Software Engineering

    Barbara Kitchenham;Lech Madeyski;David Budgen;Jacky Keung

  • Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

    E. Kocaguneli;T. Menzies;A. Bener;J. W. Keung

  • MAHAKIL: diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction

    Kwabena Ebo Bennin;Jacky Keung;Passakorn Phannachitta;Akito Monden

  • Systematic literature review and empirical investigation of barriers to process improvement in global software development

    Arif Ali Khan;Jacky Keung;Mahmood Niazi;Shahid Hussain

  • Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation

    J.W. Keung;B.A. Kitchenham;D.R. Jeffery

  • Evaluating guidelines for reporting empirical software engineering studies

    Barbara Kitchenham;Hiyam Al-Khilidar;Muhammed Ali Babar;Mike Berry

  • Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction

    Shuo Feng;Jacky Keung;Xiao Yu;Yan Xiao

  • Systematic review of success factors and barriers for software process improvement in global software development

    Arif Ali Khan;Jacky Keung

  • COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction

    Shuo Feng;Jacky Keung;Xiao Yu;Yan Xiao

  • Application migration to cloud: a taxonomy of critical factors

    Van Tran;Jacky Keung;Anna Liu;Alan Fekete

  • Active learning and effort estimation: Finding the essential content of software effort estimation data

    E. Kocaguneli;T. Menzies;J. Keung;D. Cok

  • Improving bug localization with word embedding and enhanced convolutional neural networks

    Yan Xiao;Jacky Keung;Kwabena Ebo Bennin;Qing Mi

  • On the relative value of data resampling approaches for software defect prediction

    Kwabena Ebo Bennin;Jacky W. Keung;Akito Monden

  • SPIIMM: Toward a Model for Software Process Improvement Implementation and Management in Global Software Development

    Arif Ali Khan;Jacky W. Keung;Fazal-E-Amin;M. Abdullah-Al-Wadud

  • Finding conclusion stability for selecting the best effort predictor in software effort estimation

    Jacky Keung;Ekrem Kocaguneli;Tim Menzies

  • Software design patterns classification and selection using text categorization approach

    Shahid Hussain;Jacky Keung;Arif Ali Khan

  • Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study

    Jianglin Huang;Jacky Wai Keung;Federica Sarro;Yan-Fu Li

  • Software Development Cost Estimation Using Analogy: A Review

    Jacky Keung

Frequent Co-Authors

Arif Ali Khan
Arif Ali Khan University of Oulu
Tim Menzies
Tim Menzies North Carolina State University
Kenichi Matsumoto
Kenichi Matsumoto Nara Institute of Science and Technology
Barbara Kitchenham
Barbara Kitchenham Keele University
Xiapu Luo
Xiapu Luo Hong Kong Polytechnic University
John Grundy
John Grundy Monash University
Mahmood Niazi
Mahmood Niazi King Fahd University of Petroleum and Minerals
Yun Yang
Yun Yang Swinburne University of Technology
W. K. Chan
W. K. Chan City University of Hong Kong
Yasutaka Kamei
Yasutaka Kamei Kyushu University

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