World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4962
World Ranking
10883
National Ranking
431

Weiyi Shang 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 Weiyi Shang 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 103 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Weiyi Shang 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 Weiyi Shang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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

Weiyi Shang is affiliated with the University of Waterloo in Canada and conducts research primarily in the field of Computer Science. Their work spans several subfields including Information Systems, Artificial Intelligence, Computer Networks and Communications, Software, and Computer Vision and Pattern Recognition.

The scientist's research topics encompass areas such as Software System Performance and Reliability, Software Engineering Research, Software Testing and Debugging Techniques, Topic Modeling, Cloud Computing and Resource Management, Software Reliability and Analysis Research, and Advanced Malware Detection Techniques.

Recent notable publications of Weiyi Shang include:

  • An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks (2022), ACM Transactions on Software Engineering and Methodology
  • Logram: Efficient Log Parsing Using n-Gram Dictionaries (2020), IEEE Transactions on Software Engineering
  • A Qualitative Study of the Benefits and Costs of Logging From Developers' Perspectives (2020), IEEE Transactions on Software Engineering
  • A Systematic Review of API Evolution Literature (2021), ACM Computing Surveys
  • PerfJIT: Test-Level Just-in-Time Prediction for Performance Regression Introducing Commits (2020), IEEE Transactions on Software Engineering

Frequent co-authors of Weiyi Shang include Heng Li, Zishuo Ding, Jinfu Chen, Lizhi Liao, and Tse-Hsun Chen.

Their research outputs are frequently published in venues such as IEEE Transactions on Software Engineering, arXiv (Cornell University), Empirical Software Engineering, ACM Transactions on Software Engineering and Methodology, and the Journal of Systems and Software.

Best Publications

  • Studying the effectiveness of application performance management (APM) tools for detecting performance regressions for web applications: an experience report

    Tarek M. Ahmed;Cor-Paul Bezemer;Tse-Hsun Chen;Ahmed E. Hassan

  • A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes

    Daniel Alencar da Costa;Shane McIntosh;Weiyi Shang;Uira Kulesza

  • An empirical study on inconsistent changes to code clones at the release level

    Nicolas Bettenburg;Weiyi Shang;Walid M. Ibrahim;Bram Adams

  • Assisting developers of big data analytics applications when deploying on hadoop clouds

    Weiyi Shang;Zhen Ming Jiang;Hadi Hemmati;Brain Adams

  • An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks

    Unknown

  • An exploratory study of the evolution of communicated information about the execution of large software systems

    Weiyi Shang;Zhen Ming Jiang;Bram Adams;Ahmed E. Hassan

  • Detecting performance anti-patterns for applications developed using object-relational mapping

    Tse-Hsun Chen;Weiyi Shang;Zhen Ming Jiang;Ahmed E. Hassan

  • Logram: Efficient Log Parsing Using n-Gram Dictionaries

    Hetong Dai;Heng Li;Weiyi Shang;Tse-Hsun Chen

  • Examining the stability of logging statements

    Suhas Kabinna;Cor-Paul Bezemer;Weiyi Shang;Mark D. Syer

  • Which log level should developers choose for a new logging statement

    Heng Li;Weiyi Shang;Ahmed E. Hassan

  • Studying the Impact of Clones on Software Defects

    Gehan M.K. Selim;Liliane Barbour;Weiyi Shang;Bram Adams

  • Towards just-in-time suggestions for log changes

    Heng Li;Weiyi Shang;Ying Zou;Ahmed E. Hassan

  • Studying software logging using topic models

    Heng Li;Tse-Hsun (Peter) Chen;Weiyi Shang;Ahmed E. Hassan

  • Examining the Stability of Logging Statements

    Suhas Kabinna;Weiyi Shang;Cor-Paul Bezemer;Ahmed E. Hassan

  • Logging library migrations: a case study for the apache software foundation projects

    Suhas Kabinna;Cor-Paul Bezemer;Weiyi Shang;Ahmed E. Hassan

  • A Systematic Review of API Evolution Literature

    Maxime Lamothe;Yann-Gaël Guéhéneuc;Weiyi Shang

  • Studying the relationship between logging characteristics and the code quality of platform software

    Weiyi Shang;Meiyappan Nagappan;Ahmed E. Hassan

  • A Qualitative Study of the Benefits and Costs of Logging from Developers' Perspectives

    Heng Li;Weiyi Shang;Bram Adams;Mohammed Sayagh

  • Automated Detection of Performance Regressions Using Regression Models on Clustered Performance Counters

    Weiyi Shang;Ahmed E. Hassan;Mohamed Nasser;Parminder Flora

  • MapReduce as a general framework to support research in Mining Software Repositories (MSR)

    Weiyi Shang;Zhen Ming Jiang;Bram Adams;Ahmed E. Hassan

  • CacheOptimizer: helping developers configure caching frameworks for hibernate-based database-centric web applications

    Tse-Hsun Chen;Weiyi Shang;Ahmed E. Hassan;Mohamed Nasser

  • Which log level should developers choose for a new logging statement? (journal-first abstract)

    Heng Li;Weiyi Shang;Ahmed E. Hassan

Frequent Co-Authors

Ahmed E. Hassan
Ahmed E. Hassan Queen's University
Heng Li
Heng Li Hong Kong Polytechnic University
Zhen Ming Jiang
Zhen Ming Jiang York University
Bram Adams
Bram Adams Queen's University
Emad Shihab
Emad Shihab Concordia University
Ying Zou
Ying Zou Queen's University
Meiyappan Nagappan
Meiyappan Nagappan University of Waterloo
Pooyan Jamshidi
Pooyan Jamshidi University of South Carolina
Michael W. Godfrey
Michael W. Godfrey University of Waterloo
Yasutaka Kamei
Yasutaka Kamei Kyushu University

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