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

This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Large language models (LLMs) pretrained on vast source code have achieved prominent
progress in code intelligence. However, existing code LLMs have two main limitations in …

Pre-trained models for programming languages have recently demonstrated great success
on code intelligence. To support both code-related understanding and generation tasks …

Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

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 …

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 …

Context: The correct prediction of long-lived bugs could help maintenance teams to build
their plan and to fix more bugs that often adversely affect software quality and disturb the …

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …

Backdoor attacks for neural code models have gained considerable attention due to the
advancement of code intelligence. However, most existing works insert triggers into task …