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Computer Science
Singapore
2026

D-Index & Metrics

Computer Science

D-Index
97
Citations
31009
World Ranking
429
National Ranking
7

David Lo 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 David Lo 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: 537 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

David Lo 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 David Lo 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: 97 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in Singapore Leader Award
  • 2025 - Research.com Computer Science in Singapore Leader Award
  • 2022 - Research.com Computer Science in Singapore Leader Award

Overview

David Lo is affiliated with Singapore Management University in Singapore and has a substantial publication record in the field of Computer Science, with a focus on software engineering and related subfields. Their research spans several domains including Information Systems, Artificial Intelligence, Software, Computer Networks and Communications, and Signal Processing.

The main topics covered in their work include:

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

David Lo has contributed extensively to prominent publication venues, including:

  • arXiv (Cornell University)
  • ACM Transactions on Software Engineering and Methodology
  • IEEE Transactions on Software Engineering
  • Zenodo (CERN European Organization for Nuclear Research)
  • Empirical Software Engineering

Some of their recent papers are:

  • "Large Language Models for Software Engineering: A Systematic Literature Review," 2024, ACM Transactions on Software Engineering and Methodology
  • "Defining Smart Contract Defects on Ethereum," 2020, IEEE Transactions on Software Engineering
  • "A Survey on Deep Learning for Software Engineering," 2021, ACM Computing Surveys
  • "DefectChecker: Automated Smart Contract Defect Detection by Analyzing EVM Bytecode," 2021, IEEE Transactions on Software Engineering
  • "Checking Smart Contracts With Structural Code Embedding," 2020, IEEE Transactions on Software Engineering

Their frequent coauthors include:

  • Xin Xia
  • Zhou Yang
  • Jieke Shi
  • Ferdian Thung
  • Hong Jin Kang

David Lo's research outputs indicate a strong interest in software reliability, defect detection methods, and applications of machine learning and deep learning techniques within software engineering. Their work on smart contract defects and related automated detection methods highlights an engagement with contemporary challenges in blockchain technology and software security.

Best Publications

  • Smart Contract Development: Challenges and Opportunities

    Weiqin Zou;David Lo;Pavneet Singh Kochhar;Xuan-Bach Dinh Le

  • Deep code comment generation

    Xing Hu;Ge Li;Xin Xia;David Lo

  • Where should the bugs be fixed? - more accurate information retrieval-based bug localization based on bug reports

    Jian Zhou;Hongyu Zhang;David Lo

  • Towards more accurate retrieval of duplicate bug reports

    Chengnian Sun;David Lo;Siau-Cheng Khoo;Jing Jiang

  • A discriminative model approach for accurate duplicate bug report retrieval

    Chengnian Sun;David Lo;Xiaoyin Wang;Jing Jiang

  • History Driven Program Repair

    Xuan Bach D. Le;David Lo;Claire Le Goues

  • Deep Learning for Just-in-Time Defect Prediction

    Xinli Yang;David Lo;Xin Xia;Yun Zhang

  • Practitioners' expectations on automated fault localization

    Pavneet Singh Kochhar;Xin Xia;David Lo;Shanping Li

  • Measuring program comprehension: a large-scale field study with professionals

    Xin Xia;Lingfeng Bao;David Lo;Zhenchang Xing

  • HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

    Xin Xia;David Lo;Sinno Jialin Pan;Nachiappan Nagappan

  • Duplicate bug report detection with a combination of information retrieval and topic modeling

    Anh Tuan Nguyen;Tung Thanh Nguyen;Tien N. Nguyen;David Lo

  • Version history, similar report, and structure: putting them together for improved bug localization

    Shaowei Wang;David Lo

  • Summarizing source code with transferred API knowledge

    Xing Hu;Ge Li;Xin Xia;David Lo

  • EnTagRec ++: An enhanced tag recommendation system for software information sites

    Shaowei Wang;David Lo;Bogdan Vasilescu;Alexander Serebrenik

  • Deep code comment generation with hybrid lexical and syntactical information

    Xing Hu;Ge Li;Xin Xia;David Lo

  • Classification of software behaviors for failure detection: a discriminative pattern mining approach

    David Lo;Hong Cheng;Jiawei Han;Siau-Cheng Khoo

  • Network Structure of Social Coding in GitHub

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

  • S3: syntax- and semantic-guided repair synthesis via programming by examples

    Xuan-Bach D. Le;Duc-Hiep Chu;David Lo;Claire Le Goues

  • Neural-machine-translation-based commit message generation: how far are we?

    Zhongxin Liu;Xin Xia;Ahmed E. Hassan;David Lo

  • Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction

    Yuan Tian;David Lo;Chengnian Sun

Frequent Co-Authors

Xin Xia
Xin Xia Huawei Technologies (China)
Ferdian Thung
Ferdian Thung Singapore Management University
Lingxiao Jiang
Lingxiao Jiang Singapore Management University
Ee-Peng Lim
Ee-Peng Lim Singapore Management University
Zhenchang Xing
Zhenchang Xing Australian National University
Julia Lawall
Julia Lawall French Institute for Research in Computer Science and Automation - INRIA
Tegawendé F. Bissyandé
Tegawendé F. Bissyandé University of Luxembourg
Ahmed E. Hassan
Ahmed E. Hassan Queen's University
Jacques Klein
Jacques Klein University of Luxembourg
Yves Le Traon
Yves Le Traon University of Luxembourg

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