3 citations · 4 across the 6 of their papers we have counts for
6 papers
Identifier-Free Code Embedding Models for Scalable Search
Eric Wolos, Michael Doyle
Function association is a useful process for binary reverse engineers. Search tools exist to perform association at scale, but they do not utilize the full range of capabilities th…
Can LLMs Replace Humans During Code Chunking?
Christopher Glasz, Emily Escamilla, Eric O. Scott +14
Large language models (LLMs) have become essential tools in computer science, especially for tasks involving code understanding and generation. However, existing work does not addr…
Impact of Comments on LLM Comprehension of Legacy Code
Rock Sabetto, Emily Escamilla, Devesh Agarwal +18
Large language models (LLMs) have been increasingly integrated into software engineering and maintenance tasks due to their high performance with software engineering tasks and rob…
Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
Colin Diggs, Michael Doyle, Amit Madan +18
Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. While LLMs offer promi…
Testing the Effect of Code Documentation on Large Language Model Code Understanding
William Macke, Michael Doyle
Large Language Models (LLMs) have demonstrated impressive abilities in recent years with regards to code generation and understanding. However, little work has investigated how doc…
Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments
Alexander Tapley, Marissa Dotter, Michael Doyle +5
Climate change has resulted in a year over year increase in adverse weather and weather conditions which contribute to increasingly severe fire seasons. Without effective mitigatio…