World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
80
Citations
19899
World Ranking
1101
National Ranking
158

Tao Xie 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 Tao Xie 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: 380 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Tao Xie 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 Tao Xie 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: 80 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Overview

Tao Xie is affiliated with Peking University in China and has contributed extensively to the field of computer science, with a primary focus on software and its related subfields. Their research covers areas including software testing and debugging techniques, software engineering research, and software reliability and analysis. Additionally, Tao Xie's work addresses software system performance and reliability, adversarial robustness in machine learning, advanced malware detection techniques, and advanced graph neural networks.

Their publications span a range of venues, with significant contributions to arXiv (Cornell University), Software Testing Verification and Reliability, and ACM Transactions on Software Engineering and Methodology. Other notable venues include the SSRN Electronic Journal and the 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE).

Frequently collaborating with several researchers, Tao Xie's coauthors include Robert M. Hierons, Dezhi Ran, Qianxiang Wang, Zibin Zheng, and Wing Lam.

Among Tao Xie's recent papers are:

  • Adversarial Attack on Large Scale Graph, 2021, IEEE Transactions on Knowledge and Data Engineering
  • Enjoy your observability: an industrial survey of microservice tracing and analysis, 2021, Empirical Software Engineering
  • Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings, 2021, 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
  • A Survey of Adversarial Learning on Graphs, 2020, arXiv (Cornell University)
  • A large-scale longitudinal study of flaky tests, 2020, Proceedings of the ACM on Programming Languages

Best Publications

  • An approach to detecting duplicate bug reports using natural language and execution information

    Xiaoyin Wang;Lu Zhang;Tao Xie;John Anvik

  • Parseweb: a programmer assistant for reusing open source code on the web

    Suresh Thummalapenta;Tao Xie

  • MAPO: Mining and Recommending API Usage Patterns

    Hao Zhong;Tao Xie;Lu Zhang;Jian Pei

  • WHYPER: towards automating risk assessment of mobile applications

    Rahul Pandita;Xusheng Xiao;Wei Yang;William Enck

  • A grey-box approach for automated GUI-model generation of mobile applications

    Wei Yang;Mukul R. Prasad;Tao Xie

  • Symstra: a framework for generating object-oriented unit tests using symbolic execution

    Tao Xie;Darko Marinov;Wolfram Schulte;David Notkin

  • Mining API patterns as partial orders from source code: from usage scenarios to specifications

    Mithun Acharya;Tao Xie;Jian Pei;Jun Xu

  • Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study

    Xiang Zhou;Xin Peng;Tao Xie;Jun Sun

  • Fitness-guided path exploration in dynamic symbolic execution

    Tao Xie;Nikolai Tillmann;Jonathan de Halleux;Wolfram Schulte

  • MAPO: mining API usages from open source repositories

    Tao Xie;Jian Pei

  • AppContext: differentiating malicious and benign mobile app behaviors using context

    Wei Yang;Xusheng Xiao;Benjamin Andow;Sihan Li

  • DSD-Crasher: A hybrid analysis tool for bug finding

    Christoph Csallner;Yannis Smaragdakis;Tao Xie

  • Where do developers log? an empirical study on logging practices in industry

    Qiang Fu;Jieming Zhu;Wenlu Hu;Jian-Guang Lou

  • Inferring Resource Specifications from Natural Language API Documentation

    Hao Zhong;Lu Zhang;Tao Xie;Hong Mei

  • Latent error prediction and fault localization for microservice applications by learning from system trace logs

    Xiang Zhou;Xin Peng;Tao Xie;Jun Sun

  • Identifying security bug reports via text mining: An industrial case study

    Michael Gegick;Pete Rotella;Tao Xie

  • How do software engineers understand code changes?: an exploratory study in industry

    Yida Tao;Yingnong Dang;Tao Xie;Dongmei Zhang

  • Mining succinct and high-coverage API usage patterns from source code

    Jue Wang;Yingnong Dang;Hongyu Zhang;Kai Chen

  • A fault model and mutation testing of access control policies

    Evan Martin;Tao Xie

  • Rostra: a framework for detecting redundant object-oriented unit tests

    Tao Xie;D. Notkin;D. Marinov

  • Data Mining for Software Engineering

    Tao Xie;S. Thummalapenta;D. Lo;Chao Liu

Frequent Co-Authors

Nikolai Tillmann
Nikolai Tillmann Facebook (United States)
Hong Mei
Hong Mei Peking University
Dongmei Zhang
Dongmei Zhang Microsoft (United States)
Lu Zhang
Lu Zhang Peking University
David Notkin
David Notkin University of Washington
Xuanzhe Liu
Xuanzhe Liu Peking University
Jian-Guang Lou
Jian-Guang Lou Microsoft (United States)
Wolfram Schulte
Wolfram Schulte Microsoft (United States)
Darko Marinov
Darko Marinov University of Illinois at Urbana-Champaign
Gang Huang
Gang Huang Peking University

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