31 citations · 58 across the 5 of their papers we have counts for
6 papers · 1 filter
Predictive Synthesis of API-Centric Code
Daye Nam, Baishakhi Ray, Seohyun Kim +2
Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program syn…
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…
Code Prediction by Feeding Trees to Transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian +1
We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. First, we report that using the recently proposed Transform…
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…