Denys Poshyvanyk

Full Professor

William & Mary

United States

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About

Denys Poshyvanyk is a Full Professor at William & Mary, USA. His research focuses on software engineering, artificial intelligence in software development, and the effectiveness of machine learning models in code generation and analysis. Recent publications include studies on software licensing issues related to generative AI and innovative approaches to mobile GUI testing.

Articles (18)

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2025

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2020

Collaborators (13)

Amit Seal Ami

Assistant Professor

University of South Florida

UNITED STATES

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Iftekhar Ahmed

Associate Professor

University of California, Irvine

UNITED STATES

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Aldeida Aleti

Senior Lecturer

MONASH UNIVERSITY

AUSTRALIA

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Massimiliano Di Penta

University of Sannio

ITALY

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Andrian (Andi) Marcus

University of Texas at Dallas

UNITED STATES

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Mattia Fazzini

Faculty, Assistant Professor

University of Minnesota

UNITED STATES

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Gabriele BAVOTA

Università del Salento

SWITZERLAND

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Antonio Mastropaolo

Assistant Professor

College of William and Mary

UNITED STATES

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Haipeng Cai

Washington State University

UNITED STATES

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Birgit Penzenstadler

Chalmers University of Technology

SWEDEN

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Silvia Abrahão

Profesor Titular de Universidad (Associate Professor)

Universitat Politècnica de València

SPAIN

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Laura Heymann

James G. Cutler Professor of Law

William & Mary Law School

UNITED STATES

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MAURO PEZZÈ

Professor

Università della Svizzera italiana Facoltà di scienze informatiche

SWITZERLAND

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Denys Poshyvanyk

Research Interests

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Articles (18)

Artificial Intelligence for Software Engineering: The Journey So Far and the Road Ahead

Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development

A 2030 Roadmap for Software Engineering

On the Effectiveness of LLM-as-a-Judge for Code Generation and Summarization

GUIPilot: A Consistency-Based Mobile GUI Testing Approach for Detecting Application-Specific Bugs

“The Law Doesn’t Work Like a Computer”: Exploring Software Licensing Issues Faced by Legal Practitioners

Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs

Toward a Theory of Causation for Interpreting Neural Code Models

Translating Video Recordings of Complex Mobile App UI Gestures into Replayable Scenarios

Using Transfer Learning for Code-Related Tasks

Enabling Mutant Generation for Open- and Closed-Source Android Apps

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

Enhancing Mobile App Bug Reporting via Real-time Understanding of Reproduction Steps

Automatically Assessing Code Understandability

Security in centralized data store-based home automation platforms: A systematic analysis of nESt and hue

SequenceR: Sequence-to-sequence learning for end-to-end program repair

Systematic Mutation-Based Evaluation of the Soundness of Security-Focused Android Static Analysis Techniques

Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps

Collaborators (13)

Amit Seal Ami

Iftekhar Ahmed

Aldeida Aleti

Massimiliano Di Penta

Andrian (Andi) Marcus

Mattia Fazzini

Gabriele BAVOTA

Antonio Mastropaolo

Haipeng Cai

Birgit Penzenstadler

Silvia Abrahão

Laura Heymann

MAURO PEZZÈ

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