L2M – RepGen: Towards Automated Reproduction of Deep Learning Bugs Leveraging an Intelligent Agent

This project aims to explore whether RepGen, an academic tool that helps software teams automatically reproduce bugs in deep learning and AI systems, can become a useful product for industry. By interviewing more than 100 developers, companies, and technical teams, the project will identify who needs this tool the most, what features they care about, and how RepGen can fit into real software development workflows. The results will guide the creation of a business model, a product roadmap, and a list of early adopters. This work will benefit the partner organization, Lab2Market, by providing clear, evidence-based insight into the commercial potential of RepGen and supporting their goal of turning university research into practical, market-ready innovations.

Faculty Supervisor:

Masud Rahman

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program: