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The task of repository-level code completion is to continue writing theunfinished code based on a broader context of the repository. While forautomated code completion tools, it is difficult to utilize the usefulinformation scattered in different files. We propose RepoCoder, a simple,generic, and effective framework to address the challenge. It streamlines therepository-level code completion process by incorporating a similarity-basedretriever and a pre-trained code language model in an iterativeretrieval-generation pipeline. RepoCoder makes effective utilization ofrepository-level information for code completion and has the ability togenerate code at various levels of granularity. Moreover, we propose a newbenchmark RepoEval, which consists of the latest and high-quality real-worldrepositories covering line, API invocation, and function body completionscenarios. Experimental results indicate that RepoCoder significantly improvesthe In-File completion baseline by over 10% in all settings and consistentlyoutperforms the vanilla retrieval-augmented code completion approach.Furthermore, we validate the effectiveness of RepoCoder through comprehensiveanalysis, providing valuable insights for future research. Our source code andbenchmark are publicly available:https://github.com/microsoft/CodeT/tree/main/RepoCoder

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation