Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

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 …

Large language models (LLMs) have recently been applied in software engineering to
perform tasks such as translating code between programming languages, generating code …

Software engineering is a domain characterized by intricate decision-making processes,
often relying on nuanced intuition and consultation. Recent advancements in deep learning …

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 …

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

The task of generating code from a natural language description, or NL2Code, is considered
a pressing and significant challenge in code intelligence. Thanks to the rapid development …

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 …

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 …