Leveraging Large Language Models to Investigate Machine Learning Specific Code Smells: An Empirical Study

This project seeks to improve the understanding of how Machine-Learning (ML)-specific code issues arise during software development by using Large Language Models to analyze developers’ code changes over time. An automated framework will be created to track code updates, detect ML-related issues, and identify the type of developer activity (feature development, bug fixing, enhancement, or refactoring) that introduced them. The project will strengthen collaboration between the institutions through shared supervision of the intern and interactions, and discussions of ongoing experiments and results. The results, datasets, and tools produced during the project will lay the foundation for long-term collaboration, student co-supervision, and future funding opportunities.

Faculty Supervisor:

Zadia Codabux

Student:

Partner:

University of Salerno

Discipline:

Computer science

Sector:

Information and Communications Technology (ICT); Artificial Intelligence

University:

University of Saskatchewan

Program: