World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
6155
World Ranking
9359
National Ranking
369

Ali Ouni 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 Ali Ouni 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: 204 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Ali Ouni 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 Ali Ouni 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Ali Ouni is a researcher affiliated with the École de Technologie Supérieure in Canada. The primary focus of their work lies within the domain of Computer Science, with numerous contributions across various subfields and topics related to the discipline.

Their research spans multiple specialized areas within Computer Science, including:

  • Information Systems
  • Software
  • Computer Networks and Communications
  • Computer Science Applications
  • Artificial Intelligence

Ouni's work frequently targets software engineering and related concerns, with main topics covering:

  • Software Engineering Research
  • Software Reliability and Analysis Research
  • Software System Performance and Reliability
  • Software Engineering Techniques and Practices
  • Software Testing and Debugging Techniques
  • Open Source Software Innovations
  • Advanced Malware Detection Techniques

Regarding publication venues, Ali Ouni has contributed extensively to a diverse set of platforms, including:

  • arXiv (Cornell University)
  • Empirical Software Engineering
  • Information and Software Technology
  • SSRN Electronic Journal
  • Zenodo (CERN European Organization for Nuclear Research)

Among the recent research papers authored by or involving Ali Ouni are:

  • Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting, 2020, Energies
  • Deep reinforcement learning for traffic signal control with consistent state and reward design approach, 2023, Knowledge-Based Systems
  • Test Smell Detection Tools: A Systematic Mapping Study, 2021, Evaluation and Assessment in Software Engineering
  • On the distribution of test smells in open source android applications: An exploratory study, 2020, Espace ÉTS (ETS)
  • An Interactive and Dynamic Search-Based Approach to Software Refactoring Recommendations, 2020, IEEE Transactions on Software Engineering

Their collaborative network includes several frequent co-authors:

  • Mohamed Wiem Mkaouer
  • Eman Abdullah AlOmar
  • Christian D. Newman
  • Anthony Peruma
  • Moataz Chouchen

Best Publications

  • Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

    Salah Bouktif;Ali Fiaz;Ali Ouni;Mohamed Adel Serhani

  • Do developers update their library dependencies

    Raula Gaikovina Kula;Daniel M. German;Ali Ouni;Ali Ouni;Takashi Ishio

  • Many-Objective Software Remodularization Using NSGA-III

    Wiem Mkaouer;Marouane Kessentini;Adnan Shaout;Patrice Koligheu

  • Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

    Salah Bouktif;Ali Fiaz;Ali Ouni;Mohamed Adel Serhani

  • Maintainability defects detection and correction: a multi-objective approach

    Ali Ouni;Marouane Kessentini;Houari Sahraoui;Mounir Boukadoum

  • Multi-Criteria Code Refactoring Using Search-Based Software Engineering: An Industrial Case Study

    Ali Ouni;Marouane Kessentini;Houari Sahraoui;Katsuro Inoue

  • A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection

    Wael Kessentini;Marouane Kessentini;Houari A. Sahraoui;Slim Bechikh

  • Design Defects Detection and Correction by Example

    Marouane Kessentini;Wael Kessentini;Houari Sahraoui;Mounir Boukadoum

  • Search-Based Web Service Antipatterns Detection

    Ali Ouni;Marouane Kessentini;Katsuro Inoue;Mel O Cinneide

  • Search-based software library recommendation using multi-objective optimization

    Ali Ouni;Raula Gaikovina Kula;Marouane Kessentini;Takashi Ishio

  • tsDetect: an open source test smells detection tool

    Anthony Peruma;Khalid Almalki;Christian D. Newman;Mohamed Wiem Mkaouer

  • Search-Based Peer Reviewers Recommendation in Modern Code Review

    Ali Ouni;Raula Gaikovina Kula;Katsuro Inoue

  • Search-based refactoring: Towards semantics preservation

    Ali Ouni;Marouane Kessentini;Houari Sahraoui;Mohamed Salah Hamdi

  • Web Service Antipatterns Detection Using Genetic Programming

    Ali Ouni;Raula Gaikovina Kula;Marouane Kessentini;Katsuro Inoue

  • Improving multi-objective code-smells correction using development history

    Ali Ouni;Marouane Kessentini;Houari Sahraoui;Katsuro Inoue

  • Many-Objective Software Remodularization using NSGA-III

    Mohamed Wiem Mkaouer;Marouane Kessentini;Adnan Shaout;Patrice Koligheu

  • MORE: A multi-objective refactoring recommendation approach to introducing design patterns and fixing code smells

    Ali Ouni;Ali Ouni;Marouane Kessentini;Mel Ó Cinnéide;Houari A. Sahraoui

  • Test smell detection tools: A systematic mapping study

    Wajdi Aljedaani;Anthony Peruma;Ahmed Aljohani;Mazen Alotaibi

  • The use of development history in software refactoring using a multi-objective evolutionary algorithm

    Ali Ouni;Marouane Kessentini;Houari Sahraoui;Mohamed Salah Hamdi

  • Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

    Salah Bouktif;Ali Fiaz;Ali Ouni;Mohamed Adel Serhani

  • Search-Based Refactoring Using Recorded Code Changes

    A. Ouni;M. Kessentini;H. Sahraoui

  • On the distribution of test smells in open source Android applications: an exploratory study

    Anthony Peruma;Khalid Almalki;Christian D. Newman;Mohamed Wiem Mkaouer

Frequent Co-Authors

Marouane Kessentini
Marouane Kessentini University of Michigan–Ann Arbor
Mohamed Wiem Mkaouer
Mohamed Wiem Mkaouer Rochester Institute of Technology
Katsuro Inoue
Katsuro Inoue Ritsumeikan University
Houari Sahraoui
Houari Sahraoui University of Montreal
Daniel M. German
Daniel M. German University of Victoria
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Slim Bechikh
Slim Bechikh University of Nottingham
Fabio Palomba
Fabio Palomba University of Salerno
David Lo
David Lo Singapore Management University
Manuel Wimmer
Manuel Wimmer Johannes Kepler University of Linz

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