World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
9323
World Ranking
10503
National Ranking
118

Shin Yoo 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 Shin Yoo 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: 129 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Shin Yoo 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 Shin Yoo 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Shin Yoo is affiliated with the Korea Advanced Institute of Science and Technology in South Korea and has an extensive research portfolio in computer science, focusing primarily on software and related subfields. Their scholarly contributions cover a range of topics in software engineering and artificial intelligence, supported by numerous publications in both journals and conference proceedings.

The scientist's recent papers include notable works such as:

  • "Pandemic programming" (2020) published in Empirical Software Engineering
  • "Arachne: Search-Based Repair of Deep Neural Networks" (2022) published in ACM Transactions on Software Engineering and Methodology
  • "A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization" (2024) published in Proceedings of the ACM on Software Engineering
  • "Pandemic Programming: How COVID-19 affects software developers and how their organizations can help" (2020) published in PubMed
  • "Large Language Models for Software Engineering: Survey and Open Problems" (2023) published on arXiv (Cornell University)

Shin Yoo frequently collaborates with other researchers in the field. Prominent co-authors include Gabin An, Robert Feldt, Jin-Han Kim, Sungmin Kang, and Juyeon Yoon. Such collaborations have supported a broad array of research outputs in software engineering and related domains.

The scientist's publications are often found in venues such as:

  • arXiv (Cornell University)
  • ACM Transactions on Software Engineering and Methodology
  • Empirical Software Engineering
  • ACM SIGSOFT Software Engineering Notes
  • Proceedings of the ACM on Software Engineering

Their expertise spans several primary fields of study, largely centered on computer science with detailed work in:

  • Software
  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

Within these domains, their research topics frequently address:

  • Software Testing and Debugging Techniques
  • Software Engineering Research
  • Software Reliability and Analysis Research
  • Software System Performance and Reliability
  • Adversarial Robustness in Machine Learning
  • Topic Modeling
  • Advanced Neural Network Applications

Best Publications

  • Regression testing minimization, selection and prioritization: a survey

    S. Yoo;M. Harman

  • The Oracle Problem in Software Testing: A Survey

    Earl T. Barr;Mark Harman;Phil McMinn;Muzammil Shahbaz

  • Search based software engineering: techniques, taxonomy, tutorial

    Mark Harman;Phil McMinn;Jerffeson Teixeira de Souza;Shin Yoo

  • Pareto efficient multi-objective test case selection

    Shin Yoo;Mark Harman

  • Guiding deep learning system testing using surprise adequacy

    Jinhan Kim;Robert Feldt;Shin Yoo

  • Ask the Mutants: Mutating Faulty Programs for Fault Localization

    Seokhyeon Moon;Yunho Kim;Moonzoo Kim;Shin Yoo

  • Pandemic programming: How COVID-19 affects software developers and how their organizations can help.

    Paul Ralph;Sebastian Baltes;Gianisa Adisaputri;Richard Torkar;Richard Torkar

  • Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge

    Shin Yoo;Mark Harman;Paolo Tonella;Angelo Susi

  • FLUCCS: using code and change metrics to improve fault localization

    Jeongju Sohn;Shin Yoo

  • Regression Testing Minimisation, Selection and Prioritisation - A Survey

    S Yoo;M Harman

  • Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation

    Shin Yoo;Mark Harman

  • Evolving human competitive spectra-based fault localisation techniques

    Shin Yoo

  • Test Set Diameter: Quantifying the Diversity of Sets of Test Cases

    Robert Feldt;Simon Poulding;David Clark;Shin Yoo

  • Mining Fix Patterns for FindBugs Violations

    Kui Liu;Dongsun Kim;Tegawende F. Bissyande;Shin Yoo

  • Efficiency and early fault detection with lower and higher strength combinatorial interaction testing

    Justyna Petke;Shin Yoo;Myra B. Cohen;Mark Harman

  • Fault localization prioritization: Comparing information-theoretic and coverage-based approaches

    Shin Yoo;Mark Harman;David Clark

  • Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection

    Justyna Petke;Myra B. Cohen;Mark Harman;Shin Yoo

  • Optimizing for the Number of Tests Generated in Search Based Test Data Generation with an Application to the Oracle Cost Problem

    Mark Harman;Sung Gon Kim;Kiran Lakhotia;Phil McMinn

  • Are mutation scores correlated with real fault detection?: a large scale empirical study on the relationship between mutants and real faults

    Mike Papadakis;Donghwan Shin;Shin Yoo;Doo-Hwan Bae

  • Provably Optimal and Human-Competitive Results in SBSE for Spectrum Based Fault Localisation

    Xiaoyuan Xie;Fei-Ching Kuo;Tsong Yueh Chen;Shin Yoo

  • Empirical evaluation of pareto efficient multi-objective regression test case prioritisation

    Michael G. Epitropakis;Shin Yoo;Mark Harman;Edmund K. Burke

Frequent Co-Authors

Mark Harman
Mark Harman University College London
Robert Feldt
Robert Feldt Chalmers University of Technology
Jens Krinke
Jens Krinke University College London
David Binkley
David Binkley Loyola University Maryland
Phil McMinn
Phil McMinn University of Sheffield
William B. Langdon
William B. Langdon University College London
David M. Clark
David M. Clark University of Oxford
Tsong Yueh Chen
Tsong Yueh Chen Swinburne University of Technology
Myra B. Cohen
Myra B. Cohen Iowa State University
Burak Turhan
Burak Turhan Monash University

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