Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Mohamed Wiem Mkaouer

Assistant Professor

Rochester Institute of Technology

United States

Research Interests

Explore related searches

Professors in United States Professors in Machine Learning Machine Learning professors in United States

About

Dr. Mohamed Wiem Mkaouer is an Assistant Professor at the Rochester Institute of Technology in the United States. His research interests encompass software engineering, focusing on areas such as multi-label classification, clone refactoring, and the dynamics of developer experience in code refactoring. He has authored several articles, including works on code review, software library migration, and data-driven approaches to team formation.

Articles (20)

Year:

2026

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Collaborators (3)

Christian Newman

Assistant Professor

Rochester Institute of Technology

UNITED STATES

View details

Ali Ouni

University of Quebec

CANADA

View details

Anthony Peruma

Assistant Professor

University of Hawaii at Manoa

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Mohamed Wiem Mkaouer

Research Interests

Explore related searches

About

Articles (20)

Hierarchical multi-label classification for concrete defects: An industrial case study at Vermeg

AntiCopyPaster 3.0: Just-in-Time Clone Refactoring

From apologies to insights: extracting topics from ChatGPT apologetic responses

MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search

Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

What Constitutes the Deployment and Runtime Configuration System? An Empirical Study on OpenStack Projects

PR-DupliChecker: detecting duplicate pull requests in Fork-based workflows

Third-party software library migration at the method-level using multi-objective evolutionary search

A Data-Driven Approach to Team Formation in Software Engineering Based on Personality Traits

Duplicate Bug Report Detection Using an Attention-Based Neural Language Model

Improving the detection of community smells through socio‐technical and sentiment analysis

Just-in-time code duplicates extraction

On the use of textual feature extraction techniques to support the automated detection of refactoring documentation

Refactoring for reuse: an empirical study

How do i refactor this? An empirical study on refactoring trends and topics in Stack Overflow

SATDBailiff-mining and tracking self-admitted technical debt

An Exploratory Study on Refactoring Documentation in Issues Handling

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Code Review Practices for Refactoring Changes: An Empirical Study on OpenStack

Behind the scenes: On the relationship between developer experience and refactoring

Collaborators (3)

Christian Newman

Ali Ouni

Anthony Peruma

Make your next move.