13 citations · 49 across the 10 of their papers we have counts for
5 papers · 1 filter
Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation
Sijie Shen, Xiang Zhu, Yihong Dong +3
Code generation aims to generate a code snippet automatically from natural language descriptions. Generally, the mainstream code generation methods rely on a large amount of paired…
AixBench: A Code Generation Benchmark Dataset
Yiyang Hao, Ge Li, Yongqiang Liu +5
We present a benchmark dataset for evaluating method-level code generation task. The benchmark contains a dataset of 175 samples for automated evaluation and a dataset of 161 sampl…
What does Transformer learn about source code?
Kechi Zhang, Ge Li, Zhi Jin
In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many task…
Learning to Recommend Method Names with Global Context
Fang Liu, Ge Li, Zhiyi Fu +3
In programming, the names for the program entities, especially for the methods, are the intuitive characteristic for understanding the functionality of the code. To ensure the read…
Building Program Vector Representations for Deep Learning
Lili Mou, Ge Li, Yuxuan Liu +4
Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated feature…