In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Benchmark datasets have a significant impact on accelerating research in programming
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …

Generating a readable summary that describes the functionality of a program is known as
source code summarization. In this task, learning code representation by modeling the …

Automatic source code summarization is the task of generating natural language
descriptions for source code. Automatic code summarization is a rapidly expanding research …

Recent years have seen the successful application of large pre-trained models to code
representation learning, resulting in substantial improvements on many code-related …

Source code summarization--creating natural language descriptions of source code
behavior--is a rapidly-growing research topic with applications to automatic documentation …

Code completion, which aims to predict the following code token (s) according to the code
context, can improve the productivity of software development. Recent work has proved that …

Software developers write a lot of source code and documentation during software
development. Intrinsically, developers often recall parts of source code or code summaries …