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

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

The task of repository-level code completion is to continue writing the unfinished code based
on a broader context of the repository. While for automated code completion tools, it is …

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 …

Large Language Models for Code (Code LLM) are flourishing. New and powerful models
are released on a weekly basis, demonstrating remarkable performance on the code …

Code Generation aims at generating relevant code fragments according to given natural
language descriptions. In the process of software development, there exist a large number of …

Abstract Language models of code (LMs) work well when the surrounding code provides
sufficient context. This is not true when it becomes necessary to use types, functionality or …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Automatically generating source code from natural language descriptions has been a
growing field of research in recent years. However, current large-scale code generation …

Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with
normal length. Recently, multiple studies have committed to extending the context length …