RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems

• URL: http://arxiv.org/abs/2306.03091v2
• Date: Wed, 4 Oct 2023 01:13:49 GMT
• Title: RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
• Authors: Tianyang Liu, Canwen Xu, Julian McAuley
• Abstract summary: RepoBench is a benchmark for evaluating code auto-completion systems. It consists of three evaluation tasks: RepoBench-R (Retrieval), RepoBench-C (Code Completion), and RepoBench-P (Pipeline)
• Score: 43.797002322559834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers. However, current benchmarks mainly focus on single-file tasks, leaving an assessment gap for more complex, real-world, multi-file programming scenarios. To fill this gap, we introduce RepoBench, a new benchmark specifically designed for evaluating repository-level code auto-completion systems. RepoBench supports both Python and Java and consists of three interconnected evaluation tasks: RepoBench-R (Retrieval), RepoBench-C (Code Completion), and RepoBench-P (Pipeline). Each task respectively measures the system's ability to retrieve the most relevant code snippets from other files as cross-file context, predict the next line of code with cross-file and in-file context, and handle complex tasks that require a combination of both retrieval and next-line prediction. RepoBench aims to facilitate a more complete comparison of performance and encouraging continuous improvement in auto-completion systems. RepoBench is publicly available at https://github.com/Leolty/repobench.

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