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Denys Poshyvanyk

Denys Poshyvanyk

D-Index & Metrics

Computer Science

D-Index
87
Citations
20833
World Ranking
741
National Ranking
389

Overview

Denys Poshyvanyk is affiliated with William & Mary in the United States. Their research work is situated primarily in the field of Computer Science, with a focus on several subfields and topics related to software engineering and artificial intelligence.

The scientist's recent papers include:

  • Using pre-trained models to boost code review automation, 2022, Proceedings of the 44th International Conference on Software Engineering
  • A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research, 2022, ACM Transactions on Software Engineering and Methodology
  • Using Transfer Learning for Code-Related Tasks, 2022, IEEE Transactions on Software Engineering
  • Toward interactive bug reporting for (android app) end-users, 2022, Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
  • Why Crypto-detectors Fail: A Systematic Evaluation of Cryptographic Misuse Detection Techniques, 2022, 2022 IEEE Symposium on Security and Privacy (SP)

These recent publications reflect work across multiple venues, including:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • ACM Transactions on Software Engineering and Methodology
  • IEEE Transactions on Software Engineering
  • Proceedings of the ACM on software engineering.

Denys Poshyvanyk's research interests cover significant topics and fields within software engineering. The main fields of study include:

  • Computer Science

Subfields of study addressed in their work include:

  • Information Systems
  • Artificial Intelligence
  • Software
  • Signal Processing
  • Computer Science Applications

Their focus within these areas encompasses topics such as:

  • Software Engineering Research
  • Advanced Malware Detection Techniques
  • Software Testing and Debugging Techniques
  • Natural Language Processing Techniques
  • Topic Modeling
  • Law, AI, and Intellectual Property
  • Software Engineering Techniques and Practices

Frequent collaborators of this scientist include:

  • Gabriele Bavota
  • Oscar Chaparro
  • Nathan Cooper
  • Kevin Moran
  • Michele Tufano

Best Publications

  • Feature location in source code: a taxonomy and survey

    Bogdan Dit;Meghan Revelle;Malcom Gethers;Denys Poshyvanyk

  • Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

    D. Poshyvanyk;Y.-G. Gueheneuc;A. Marcus;G. Antoniol

  • Deep learning code fragments for code clone detection

    Martin White;Michele Tufano;Christopher Vendome;Denys Poshyvanyk

  • SequenceR : Sequence-to-Sequence Learning for End-to-End Program Repair

    Zimin Chen;Steve Kommrusch;Michele Tufano;Louis-Noel Pouchet

  • Portfolio: finding relevant functions and their usage

    Collin McMillan;Mark Grechanik;Denys Poshyvanyk;Qing Xie

  • Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

    A. Marcus;D. Poshyvanyk;R. Ferenc

  • Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

    D. Poshyvanyk;A. Marcus

  • API change and fault proneness: a threat to the success of Android apps

    Mario Linares-Vásquez;Gabriele Bavota;Carlos Bernal-Cárdenas;Massimiliano Di Penta

  • How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms

    Annibale Panichella;Bogdan Dit;Rocco Oliveto;Massimilano Di Penta

  • Mining Version Histories for Detecting Code Smells

    Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Rocco Oliveto

  • An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

    Michele Tufano;Cody Watson;Gabriele Bavota;Massimiliano Di Penta

  • Feature location via information retrieval based filtering of a single scenario execution trace

    Dapeng Liu;Andrian Marcus;Denys Poshyvanyk;Vaclav Rajlich

  • Detecting bad smells in source code using change history information

    Fabio Palomba;Gabriele Bavota;Massimiliano Di Penta;Rocco Oliveto

  • When and why your code starts to smell bad

    Michele Tufano;Fabio Palomba;Gabriele Bavota;Rocco Oliveto

  • Mining energy-greedy API usage patterns in Android apps: an empirical study

    Mario Linares-Vásquez;Gabriele Bavota;Carlos Bernal-Cárdenas;Rocco Oliveto

  • Toward deep learning software repositories

    Martin White;Christopher Vendome;Mario Linares-Vasquez;Denys Poshyvanyk

  • Using information retrieval based coupling measures for impact analysis

    Denys Poshyvanyk;Andrian Marcus;Rudolf Ferenc;Tibor Gyimóthy

  • When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)

    Michele Tufano;Fabio Palomba;Gabriele Bavota;Rocco Oliveto

  • Automatically Discovering, Reporting and Reproducing Android Application Crashes

    Kevin Moran;Mario Linares-Vasquez;Carlos Bernal-Cardenas;Christopher Vendome

  • On the Equivalence of Information Retrieval Methods for Automated Traceability Link Recovery

    Rocco Oliveto;Malcom Gethers;Denys Poshyvanyk;Andrea De Lucia

  • The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps

    Gabriele Bavota;Mario Linares-Vasquez;Carlos Eduardo Bernal-Cardenas;Massimiliano Di Penta

Frequent Co-Authors

Mario Linares-Vasquez
Mario Linares-Vasquez Universidad de Los Andes
Gabriele Bavota
Gabriele Bavota Universita della Svizzera Italiana
Rocco Oliveto
Rocco Oliveto University of Molise
Massimiliano Di Penta
Massimiliano Di Penta University of Sannio
Andrea De Lucia
Andrea De Lucia University of Salerno
Andrian Marcus
Andrian Marcus The University of Texas at Dallas
Fabio Palomba
Fabio Palomba University of Salerno
Daniel M. German
Daniel M. German University of Victoria
Jane Cleland-Huang
Jane Cleland-Huang University of Notre Dame
Giuliano Antoniol
Giuliano Antoniol Polytechnique Montréal

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