Abstract
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 difficult to utilize the useful information scattered in different files. We propose RepoCoder, a simple, generic, and effective framework to address the challenge. It streamlines the repository-level code completion process by incorporating a similarity-based retriever and a pre-trained code language model, which allows for the effective utilization of repository-level information for code completion and grants the ability to generate code at various levels of granularity. Furthermore, RepoCoder utilizes a novel iterative retrieval-generation paradigm that bridges the gap between retrieval context and the intended completion target. We also propose a new benchmark RepoEval, which consists of the latest and high-quality real-world repositories covering line, API invocation, and function body completion scenarios. We test the performance of RepoCoder by using various combinations of code retrievers and generators. Experimental results indicate that RepoCoder significantly improves the zero-shot code completion baseline by over 10% in all settings and consistently outperforms the vanilla retrieval-augmented code completion approach. Furthermore, we validate the effectiveness of RepoCoder through comprehensive analysis, providing valuable insights for future research.
Abstract (translated)
存储层代码补全的任务是基于存储层上下文更广泛的情况继续编写未完成的代码。但对于自动化代码补全工具来说,很难利用分散在不同文件中的有用信息。我们提出了RepoCoder,一个简单、通用且有效的框架来解决这个挑战。它简化了存储层代码补全的过程,通过添加一个基于相似度的检索器和一个预先训练的代码语言模型,使得有效利用存储层信息进行代码补全和赋予生成代码的多种粒度的能力。此外,RepoCoder使用了一种新颖的迭代检索生成范式,以填补检索上下文和预期完成目标之间的差距。我们还提出了一个新基准RepoEval,它包括最新的高质量实际存储库,涵盖了线条、API调用和函数体完成场景。我们使用各种代码检索器和生成器的组合来测试RepoCoder的性能。实验结果显示, repoCoder在所有设置中都显著改善了零次检索代码补全基线超过10%,并始终优于简单的检索增强代码补全方法。此外,我们通过全面分析验证了RepoCoder的有效性,为未来的研究提供了宝贵的见解。
URL
https://arxiv.org/abs/2303.12570
