Fuse Lab focuses on the following research projects:

Automated Code Refactoring – This project explores how to use Large Language Models (LLMs) to automatically refactor large-scale codebases. The goal is to reduce technical debt and complexity while increasing overall code quality.

Automated Test Generation – Focused on improving software testing, this project investigates coverage effectiveness and test prioritization. It aims to define what “good coverage” means and leverages Meta’s models to improve industry standards.

Code Review – This research leverages AI to enhance the code review process. It tackles challenges such as reviewer recommendations, improving the quality of comments, automated fixing of identified issues, and reducing review latency.

Engineering Productivity Metrics – This project aims to define and capture productivity standards for software and Machine Learning Engineers (MLE). It focuses on product metrics (e.g., code complexity), process metrics (e.g., review latency), and people metrics (e.g., expertise levels).

The Dual-Use Dilemma of LLMs4Code – This project investigates the security implications of using LLMs in software engineering. It explores how these models can improve software security (e.g., binary decompilation) while also addressing risks like malicious use, data leakage, and intellectual property conflicts.