Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Recently, machine learning (ML) methods have been used to create powerful language
models for a broad range of natural language processing tasks. An important subset of this …

Recently, the ChatGPT LLM has received great attention: it can be used as a bot for
discussing source code, prompting it to suggest changes, provide descriptions or even …

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 …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Pre-trained models have been shown effective in many code intelligence tasks. These
models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream …

Source code summarization involves creating brief descriptions of source code in natural
language. These descriptions are a key component of software documentation such as …

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 …

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 aims to generate natural language summaries from structured
code snippets for better understanding code functionalities. However, automatic code …