World's Best Scientists 2026 revealed!
Shane McIntosh

Shane McIntosh

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
6527
World Ranking
8616
National Ranking
347

Shane McIntosh publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Shane McIntosh sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 145 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Shane McIntosh D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Shane McIntosh sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 36 D-Index — 13th percentile

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

The last bar groups every scientist with 107 D-Index or more.

Overview

Shane McIntosh is affiliated with McGill University in Canada and has contributed extensively to the field of computer science, particularly within software engineering research. Their body of work encompasses a variety of topics related to software reliability, system performance, testing, debugging, and advanced malware detection techniques.

Their recent publications include the following papers:

  • Accelerating Continuous Integration by Caching Environments and Inferring Dependencies, 2020, IEEE Transactions on Software Engineering
  • Lessons from eight years of operational data from a continuous integration service, 2022, Proceedings of the 44th International Conference on Software Engineering
  • Code Cloning in Smart Contracts on the Ethereum Platform: An Extended Replication Study, 2022, IEEE Transactions on Software Engineering
  • The Ghost Commit Problem When Identifying Fix-Inducing Changes: An Empirical Study of Apache Projects, 2021, IEEE Transactions on Software Engineering
  • An Empirical Study of Type-Related Defects in Python Projects, 2021, IEEE Transactions on Software Engineering

Frequent collaborators in their research include:

  • Salomé Perez-Rosero
  • Robert Dyer
  • Witawas Srisa-an
  • Mahmoud Alfadel
  • Samuel W. Flint

McIntosh has published in several prominent venues, with a notable presence in:

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

The main field of study for McIntosh is computer science, with significant contributions distributed across various subfields:

  • Information Systems
  • Software
  • Computer Networks and Communications
  • Artificial Intelligence
  • Signal Processing

Their research topics emphasize software engineering research, including specific areas such as:

  • Software Reliability and Analysis Research
  • Software System Performance and Reliability
  • Software Engineering Techniques and Practices
  • Software Testing and Debugging Techniques
  • Advanced Malware Detection Techniques
  • Scientific Computing and Data Management

Best Publications

  • An Empirical Comparison of Model Validation Techniques for Defect Prediction Models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects

    Shane McIntosh;Yasutaka Kamei;Bram Adams;Ahmed E. Hassan

  • The Impact of Automated Parameter Optimization on Defect Prediction Models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • An empirical study of the impact of modern code review practices on software quality

    Shane Mcintosh;Yasutaka Kamei;Bram Adams;Ahmed E. Hassan

  • Automated parameter optimization of classification techniques for defect prediction models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Kenichi Matsumoto

  • Revisiting the impact of classification techniques on the performance of defect prediction models

    Baljinder Ghotra;Shane McIntosh;Ahmed E. Hassan

  • Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models

    Unknown

  • Studying just-in-time defect prediction using cross-project models

    Yasutaka Kamei;Takafumi Fukushima;Shane Mcintosh;Kazuhiro Yamashita

  • Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction

    Shane McIntosh;Yasutaka Kamei

  • Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software

    Moritz Beller;Radjino Bholanath;Shane McIntosh;Andy Zaidman

  • 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 of just-in-time defect prediction using cross-project models

    Takafumi Fukushima;Yasutaka Kamei;Shane McIntosh;Kazuhiro Yamashita

  • An empirical study of build maintenance effort

    Shane McIntosh;Bram Adams;Thanh H.D. Nguyen;Yasutaka Kamei

  • Do code review practices impact design quality? A case study of the Qt, VTK, and ITK projects

    Rodrigo Morales;Shane McIntosh;Foutse Khomh

  • Revisiting code ownership and its relationship with software quality in the scope of modern code review

    Patanamon Thongtanunam;Shane McIntosh;Ahmed E. Hassan;Hajimu Iida

  • Modern Release Engineering in a Nutshell -- Why Researchers Should Care

    Bram Adams;Shane McIntosh

  • A large-scale study of the impact of feature selection techniques on defect classification models

    Baljinder Ghotra;Shane Mcintosh;Ahmed E. Hassan

  • Review participation in modern code review

    Patanamon Thongtanunam;Shane Mcintosh;Ahmed E. Hassan;Hajimu Iida

  • The impact of mislabelling on the performance and interpretation of defect prediction models

    Chakkrit Tantithamthavorn;Shane McIntosh;Ahmed E. Hassan;Akinori Ihara

  • The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models

    Unknown

  • The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models

    Feng Zhang;Ahmed E. Hassan;Shane McIntosh;Ying Zou

  • The evolution of Java build systems

    Shane Mcintosh;Bram Adams;Ahmed E. Hassan

  • Investigating code review practices in defective files: an empirical study of the Qt system

    Patanamon Thongtanunam;Shane McIntosh;Ahmed E. Hassan;Hajimu Iida

Frequent Co-Authors

Ahmed E. Hassan
Ahmed E. Hassan Queen's University
Yasutaka Kamei
Yasutaka Kamei Kyushu University
Kenichi Matsumoto
Kenichi Matsumoto Nara Institute of Science and Technology
Bram Adams
Bram Adams Queen's University
Chakkrit Tantithamthavorn
Chakkrit Tantithamthavorn Monash University
Meiyappan Nagappan
Meiyappan Nagappan University of Waterloo
Martin Pinzger
Martin Pinzger University of Klagenfurt
Audris Mockus
Audris Mockus University of Tennessee at Knoxville
Éric Tanter
Éric Tanter University of Chile
Foutse Khomh
Foutse Khomh Polytechnique Montréal

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