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 …

Code comment generation aims at generating natural language descriptions for a code
snippet to facilitate developers' program comprehension activities. Despite being studied for …

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 …

Large Language Models (LLM) are a new class of computation engines," programmed" via
prompt engineering. Researchers are still learning how to best" program" these LLMs to …

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

While a large number of pre-trained models of source code have been successfully
developed and applied to a variety of software engineering (SE) tasks in recent years, our …

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