Automatically Evolving Machine Learning Codebases with Large Language Models

Machine learning (ML) is transforming industries like IT, finance, and healthcare, but the code that powers these systems is still mostly written and updated by hand. This project explores how Large Language Models (LLMs) can assist developers by predicting and suggesting code edits for ML projects.

By analyzing real-world code from public repositories, the research will develop and test an AI-driven approach to automate ML code modifications based on developer intent. The goal is to make software development faster and more efficient, helping businesses and researchers automate the process of updating codebases.

This project aims to build a benchmark dataset of ML code edits, develop an LLM-powered assistant that can suggest and apply modifications, and evaluate its effectiveness compared to human-made changes. By improving automation in software engineering, the research will help streamline ML development and reduce manual effort.

Faculty Supervisor:

Pengyu Nie

Student:

Partner:

Ukrainian Catholic University

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

University of Waterloo

Program: