Some recently developed code large language models (Code LLMs) have been pre-trained on repository-level code data (Repo-Code LLMs), enabling these models to recognize repository structures and utilize cross-file information for code completion. However, in real-world development scenarios, simply concatenating the entire code repository often exceeds the context window limits of these Repo-Code LLMs, leading to significant performance degradation. In this study, we conducted extensive preliminary experiments and analyses on six Repo-Code LLMs. The results indicate that maintaining the topological dependencies of files and increasing the code file content in the completion prompts can improve completion accuracy; pruning the specific implementations of functions in all dependent files does not significantly reduce the accuracy of completions. Based on these findings, we proposed a strategy named Hierarchical Context Pruning (HCP) to construct completion prompts with high informational code content. The HCP models the code repository at the function level, maintaining the topological dependencies between code files while removing a large amount of irrelevant code content, significantly reduces the input length for repository-level code completion. We applied the HCP strategy in experiments with six Repo-Code LLMs, and the results demonstrate that our proposed method can significantly enhance completion accuracy while substantially reducing the length of input. Our code and data are available at https://github.com/Hambaobao/HCP-Coder.

维护文件之间的拓扑依赖性和增加代码文件内容可提高完成准确性，修剪所有相关文件中函数的特定实现并不会显著降低完成准确性。基于这些发现，我们提出了一种名为 Hierarchical Context Pruning (HCP) 的策略，以高信息代码内容构建完成提示，该策略在函数级别模拟代码存储库，减少了用于代码完成的输入长度。实验结果表明，我们的方法可以显著提高完成准确性，并大大减少输入长度。

层次上下文剪枝：通过仓库级预训练代码语言模型优化实际代码补全