3 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.SE2025★ 1 cited
RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retri…
cs.SE2025★ 3 cited
Comparing Human and LLM Generated Code: The Jury is Still Out!
Sherlock A. Licorish, Ansh Bajpai, Chetan Arora +2
Much is promised in relation to AI-supported software development. However, there has been limited evaluation effort in the research domain aimed at validating the true utility of…