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

Abstract Large Language Models (LLMs) have drawn widespread attention and research
due to their astounding performance in text generation and reasoning tasks. Derivative …

Automatic tagging on software information sites is a tag recommendation service. It aims to
recommend content-based tags for a software object to help developers make distinctions …

Code models have made significant advancements in code intelligence by encoding
knowledge about programming languages. While previous studies have explored the …

Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has
achieved great success in many software testing and analysis tasks. While effective and …

Pre-trained code representation models such as CodeBERT have demonstrated superior
performance in a variety of software engineering tasks, yet they are often heavy in …

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared
receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving …

Recent years have witnessed increasing interest in code representation learning, which
aims to represent the semantics of source code into distributed vectors. Currently, various …

The objective of pre-trained language models is to learn contextual representations of
textual data. Pre-trained language models have become mainstream in natural language …

Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …