Large language models (LLMs) have demonstrated remarkable capabilities in
code generation tasks. However, repository-level code generation presents
unique challenges, particularly due to the need to utilize information spread
across multiple files within a repository. Existing retrieval-based approaches
sometimes fall short as they are limited in obtaining a broader and deeper
repository context. In this paper, we present CatCoder, a novel code generation
framework designed for statically typed programming languages. CatCoder
enhances repository-level code generation by integrating relevant code and type
context. Specifically, it leverages static analyzers to extract type
dependencies and merges this information with retrieved code to create
comprehensive prompts for LLMs. To evaluate the effectiveness of CatCoder, we
adapt and construct benchmarks that include 199 Java tasks and 90 Rust tasks.
The results show that CatCoder outperforms the RepoCoder baseline by up to
17.35%, in terms of pass@k score. Furthermore, the generalizability of CatCoder
is assessed using various LLMs, including both code-specialized models and
general-purpose models. Our findings indicate consistent performance
improvements across all models, which underlines the practicality of CatCoder.

CatCoder 是一种新颖的代码生成框架，扩展了库级代码生成的能力，通过整合相关代码和类型上下文，利用静态分析器提取类型依赖并将其与检索到的代码合并，创建综合提示；实验证明 CatCoder 在各种 LLMs 中表现良好，提供了一致的性能改进。

增强集成上下文信息的代码生成库

Enhancing Repository-Level Code Generation with Integrated Contextual  Information

Code completion models have made significant progress in recent years.
Recently, repository-level code completion has drawn more attention in modern
software development, and several baseline methods and benchmarks have been
proposed. However, existing repository-level code completion methods often fall
short of fully using the extensive context of a project repository, such as the
intricacies of relevant files and class hierarchies. Besides, the existing
benchmarks usually focus on limited code completion scenarios, which cannot
reflect the repository-level code completion abilities well of existing
methods. To address these limitations, we propose the R2C2-Coder to enhance and
benchmark the real-world repository-level code completion abilities of code
Large Language Models, where the R2C2-Coder includes a code prompt construction
method R2C2-Enhance and a well-designed benchmark R2C2-Bench. Specifically,
first, in R2C2-Enhance, we first construct the candidate retrieval pool and
then assemble the completion prompt by retrieving from the retrieval pool for
each completion cursor position. Second, based on R2C2 -Enhance, we can
construct a more challenging and diverse R2C2-Bench with training, validation
and test splits, where a context perturbation strategy is proposed to simulate
the real-world repository-level code completion well. Extensive results on
multiple benchmarks demonstrate the effectiveness of our R2C2-Coder.

基于大型语言模型的 R2C2-Coder 方法增强和评估了真实世界的代码仓库级代码补全能力，其中 R2C2-Coder 包括 R2C2-Enhance 方法和 R2C2-Bench 基准测试。

R2C2-Coder：提升和评估代码大型语言模型的真实世界仓库级代码补全能力

R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code  Completion Abilities of Code Large Language Models

Large Language Models (LLMs) have achieved remarkable success in code
completion, as evidenced by their essential roles in developing code assistant
services such as Copilot. Being trained on in-file contexts, current LLMs are
quite effective in completing code for single source files. However, it is
challenging for them to conduct repository-level code completion for large
software projects that require cross-file information. Existing research on
LLM-based repository-level code completion identifies and integrates cross-file
contexts, but it suffers from low accuracy and limited context length of LLMs.
In this paper, we argue that Integrated Development Environments (IDEs) can
provide direct, accurate and real-time cross-file information for
repository-level code completion. We propose IDECoder, a practical framework
that leverages IDE native static contexts for cross-context construction and
diagnosis results for self-refinement. IDECoder utilizes the rich cross-context
information available in IDEs to enhance the capabilities of LLMs of
repository-level code completion. We conducted preliminary experiments to
validate the performance of IDECoder and observed that this synergy represents
a promising trend for future exploration.

IDECoder 利用 IDE 提供的跨文件信息，改进了 Large Language Models 在大型软件项目的仓库级别代码补全任务中的性能。