24 citations · 27 across the 2 of their papers we have counts for
4 papers
Improving Code Autocompletion with Transfer Learning
Wen Zhou, Seohyun Kim, Vijayaraghavan Murali +1
Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integrations. Recently, accur…
Learning Autocompletion from Real-World Datasets
Gareth Ari Aye, Seohyun Kim, Hongyu Li
Code completion is a popular software development tool integrated into all major IDEs. Many neural language models have achieved promising results in completion suggestion predicti…
Neural Code Search Evaluation Dataset
Hongyu Li, Seohyun Kim, Satish Chandra
There has been an increase of interest in code search using natural language. Assessing the performance of such code search models can be difficult without a readily available eval…
When Deep Learning Met Code Search
Jose Cambronero, Hongyu Li, Seohyun Kim +2
There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of co…