World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
4002
World Ranking
12289
National Ranking
164

Ferdian Thung 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 Ferdian Thung 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: 116 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Ferdian Thung 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 Ferdian Thung 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Ferdian Thung is affiliated with Singapore Management University in Singapore and focuses primarily on the field of Computer Science. Their research spans several subfields, notably Information Systems, Artificial Intelligence, Signal Processing, Software, and Computer Networks and Communications.

The scientist's work covers diverse topics within software and data analysis domains. Main topics include:

  • Software Engineering Research
  • Topic Modeling
  • Advanced Malware Detection Techniques
  • Software Testing and Debugging Techniques
  • Software System Performance and Reliability
  • Software Engineering Techniques and Practices
  • Web Data Mining and Analysis

Ferdian Thung has published extensively across various venues. Notable frequent publication venues include:

  • arXiv (Cornell University)
  • ACM Transactions on Software Engineering and Methodology
  • IEEE Transactions on Software Engineering
  • 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  • Empirical Software Engineering

Several recent papers reflect the focus and evolution of their research interests. These include:

  • "When and How to Make Breaking Changes," 2021, ACM Transactions on Software Engineering and Methodology
  • "BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems," 2021, IEEE Transactions on Software Engineering
  • "Duplicate Bug Report Detection: How Far Are We?," 2022, ACM Transactions on Software Engineering and Methodology
  • "AndroEvolve: automated Android API update with data flow analysis and variable denormalization," 2022, Empirical Software Engineering
  • "Representation Learning for Stack Overflow Posts: How Far Are We?," 2023, ACM Transactions on Software Engineering and Methodology

Frequent collaborators have contributed significantly to the body of work associated with Ferdian Thung. These co-authors include:

  • David Lo
  • Ting Zhang
  • Ivana Clairine Irsan
  • Lingxiao Jiang
  • Zhou Yang

Best Publications

  • Network Structure of Social Coding in GitHub

    F. Thung;T. F. Bissyande;D. Lo;Lingxiao Jiang

  • How to break an API: cost negotiation and community values in three software ecosystems

    Christopher Bogart;Christian Kästner;James Herbsleb;Ferdian Thung

  • Understanding the Test Automation Culture of App Developers

    Pavneet Singh Kochhar;Ferdian Thung;Nachiappan Nagappan;Thomas Zimmermann

  • Popularity, Interoperability, and Impact of Programming Languages in 100,000 Open Source Projects

    Tegawende F. Bissyande;Ferdian Thung;David Lo;Lingxiao Jiang

  • An Empirical Study of Bugs in Machine Learning Systems

    Ferdian Thung;Shaowei Wang;David Lo;Lingxiao Jiang

  • Automatic Defect Categorization

    Ferdian Thung;David Lo;Lingxiao Jiang

  • Automated library recommendation

    Ferdian Thung;David Lo;Julia Lawall

  • Automatic recommendation of API methods from feature requests

    Ferdian Thung;Shaowei Wang;David Lo;Julia Lawall

  • Extended comprehensive study of association measures for fault localization

    Lucia Lucia;David Lo;Lingxiao Jiang;Ferdian Thung

  • Categorizing the Content of GitHub README Files

    Gede Artha Azriadi Prana;Christoph Treude;Ferdian Thung;Thushari Atapattu

  • Overfitting in semantics-based automated program repair

    Xuan-Bach Dinh Le;Ferdian Thung;David Lo;Claire Le Goues

  • Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?

    Ting Zhang;Bowen Xu;Ferdian Thung;Stefanus Agus Haryono

  • BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies

    Ratnadira Widyasari;Sheng Qin Sim;Camellia Lok;Haodi Qi

  • Code coverage and test suite effectiveness: Empirical study with real bugs in large systems

    Pavneet Singh Kochhar;Ferdian Thung;David Lo

  • Empirical Evaluation of Bug Linking

    T. F. Bissyande;F. Thung;Shaowei Wang;D. Lo

  • Theory and Practice, Do They Match? A Case with Spectrum-Based Fault Localization

    Tien-Duy B. Le;Ferdian Thung;David Lo

  • Deep Transfer Bug Localization

    Xuan Huo;Ferdian Thung;Ming Li;David Lo

  • To what extent could we detect field defects? an empirical study of false negatives in static bug finding tools

    Ferdian Thung;Lucia;David Lo;Lingxiao Jiang

  • When and How to Make Breaking Changes: Policies and Practices in 18 Open Source Software Ecosystems

    Chris Bogart;Christian Kästner;James Herbsleb;Ferdian Thung

  • Are faults localizable

    Lucia;Ferdian Thung;David Lo;Lingxiao Jiang

  • Should I follow this fault localization tool's output?

    Tien-Duy B. Le;David Lo;Ferdian Thung

  • Detecting similar applications with collaborative tagging

    Ferdian Thung;David Lo;Lingxiao Jiang

  • [Journal First] Overfitting in Semantics-Based Automated Program Repair

    Xuan-Bach D. Le;Ferdian Thung;David Lo;Claire Le Goues

Frequent Co-Authors

David Lo
David Lo Singapore Management University
Lingxiao Jiang
Lingxiao Jiang Singapore Management University
Julia Lawall
Julia Lawall French Institute for Research in Computer Science and Automation - INRIA
Gilles Muller
Gilles Muller French Institute for Research in Computer Science and Automation - INRIA
Claire Le Goues
Claire Le Goues Carnegie Mellon University
Tegawendé F. Bissyandé
Tegawendé F. Bissyandé University of Luxembourg
Christian Kästner
Christian Kästner Carnegie Mellon University
Christoph Treude
Christoph Treude Singapore Management University
Premkumar Devanbu
Premkumar Devanbu University of California, Davis
James D. Herbsleb
James D. Herbsleb Carnegie Mellon University

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