114 citations · 165 across the 16 of their papers we have counts for
3 papers · 1 filter
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad +5
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file c…
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Yangruibo Ding, Sahil Suneja, Yunhui Zheng +4
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in…